diff --git a/ipython/Conformer Generation Workflow.ipynb b/ipython/Conformer Generation Workflow.ipynb
index f4b25044..ac89b8fb 100644
--- a/ipython/Conformer Generation Workflow.ipynb	
+++ b/ipython/Conformer Generation Workflow.ipynb	
@@ -9,8 +9,8 @@
     "Leverage ETKDG and GeoMol as 3D geometry embedder for stochastic conformer generation\n",
     "\n",
     "The idea is to have modular methods for each step, which are currently hardcoded. This includes:\n",
-    "- initial conformer embedding (ETKDG, GeoMol)\n",
-    "- optimization/energy (MMFF, UFF, GFN-FF, GFN2-xTB)\n",
+    "- initial conformer embedding (ETKDG, GeoMol, Torsional-Diffusion)\n",
+    "- optimization/energy (MMFF, UFF, GFN-FF, GFN2-xTB, other semi-empirical in QM software)\n",
     "- pruning (torsion fingerprints, CREGEN)\n",
     "- convergence metrics (conformational entropy/partition function)"
    ]
@@ -22,6 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
+    "\n",
     "from rdmc.conformer_generation.embedders import *\n",
     "from rdmc.conformer_generation.optimizers import *\n",
     "from rdmc.conformer_generation.pruners import *\n",
@@ -29,7 +31,7 @@
     "from rdmc.conformer_generation.generators import StochasticConformerGenerator\n",
     "from rdmc.conformer_generation.utils import dict_to_mol\n",
     "\n",
-    "from rdmc import RDKitMol\n",
+    "from rdmc import Mol\n",
     "from rdtools.view import mol_viewer, interactive_conformer_viewer, conformer_viewer\n",
     "\n",
     "T = 298  # K\n",
@@ -66,7 +68,7 @@
     "smi = \"[C:1]([C@@:2]([O:3][H:12])([C:4]([N:5]([C:6](=[O:7])[H:16])[H:15])([H:13])[H:14])[H:11])([H:8])([H:9])[H:10]\"  # example 1\n",
     "smi = \"CN1C2=C(C=C(C=C2)Cl)C(=NCC1=O)C3=CC=CC=C3\"  # example 2\n",
     "\n",
-    "mol_viewer(RDKitMol.FromSmiles(smi))"
+    "mol_viewer(Mol.FromSmiles(smi))"
    ]
   },
   {
@@ -87,8 +89,7 @@
     "n_confs = 10  # Number of conformers to create\n",
     "\n",
     "embedder = ETKDGEmbedder()  # Initialize conformer embedder\n",
-    "unique_mol_data = embedder(smi, n_confs)  # Embed molecule 3D geometries with ETKDG\n",
-    "mol = dict_to_mol(unique_mol_data)  # Convert raw data to a molecule object"
+    "mol = embedder(smi, n_confs)  # Embed molecule 3D geometries with ETKDG"
    ]
   },
   {
@@ -140,9 +141,8 @@
     "dataset = \"drugs\"\n",
     "device = \"cuda\"\n",
     "\n",
-    "embedder = GeoMolEmbedder(dataset=dataset, track_stats=True, temp_schedule=\"none\", device=device) # Initialize conformer embedder\n",
-    "unique_mol_data = embedder(smi, n_confs)  # Embed molecule 3D geometries with ETKDG\n",
-    "mol = dict_to_mol(unique_mol_data)  # Convert raw data to a molecule object"
+    "embedder = GeoMolEmbedder(dataset=dataset, track_stats=True, temp_schedule=\"none\", device=device)\n",
+    "mol = embedder(smi, n_confs)"
    ]
   },
   {
@@ -157,6 +157,57 @@
     "mol_viewer(mol, conf_id=visualize_conf_id)  # visualize the molecule"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf852fbd",
+   "metadata": {},
+   "source": [
+    "### 1.3 Torsional-Diffusion Embedder\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a00f0f28",
+   "metadata": {},
+   "source": [
+    "You can skip this block if you don't have Torsional-Diffusion installed. To install it,\n",
+    "\n",
+    "Clone Xiaorui's fork\n",
+    "```\n",
+    "git clone https://github.com/xiaoruiDong/torsional-diffusion\n",
+    "```\n",
+    "and then follow the instruction for installing the environment and downloading the trained models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38d85e46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_confs = 10  # Number of conformers to create\n",
+    "\n",
+    "embedder = TorsionalDiffusionEmbedder(\n",
+    "    repo_dir= Path.home() / \"Apps/torsional-diffusion\",  # path to cloned repo\n",
+    "    model_dir= Path.home() / \"Apps/torsional-diffusion/workdir/drugs_default\",  # path to downloaded model\n",
+    ")\n",
+    "\n",
+    "mol = embedder(smi, n_confs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd37bd33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "visualize_conf_id = 2\n",
+    "\n",
+    "mol_viewer(mol, conf_id=visualize_conf_id)  # visualize the molecule"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "46be5c44",
@@ -195,6 +246,9 @@
     "optimizer = MMFFOptimizer()\n",
     "# if you installed XTB, you can uncomment the following line\n",
     "# optimizer = XTBOptimizer()\n",
+    "# if you have Gaussian, Orca, or QChem, you can try the following optimizers\n",
+    "# but they can be much slower when using more expansive methods\n",
+    "# optimizer = GaussianOptimizer(nprocs=16, memory=32, method=\"PM7\")\n",
     "\n",
     "# Pruner\n",
     "pruner = TorsionPruner(max_chk_threshold=30)\n",
@@ -220,7 +274,7 @@
    "source": [
     "smi = \"CN1C2=C(C=C(C=C2)Cl)C(=NCC1=O)C3=CC=CC=C3\"\n",
     "\n",
-    "mol_viewer(RDKitMol.FromSmiles(smi))"
+    "mol_viewer(Mol.FromSmiles(smi))"
    ]
   },
   {
@@ -232,9 +286,9 @@
    },
    "outputs": [],
    "source": [
-    "n_conformers_per_iter = 100\n",
-    "min_iters = 2\n",
-    "max_iters = 5\n",
+    "n_conformers_per_iter = 10\n",
+    "min_iters = 1\n",
+    "max_iters = 3\n",
     "\n",
     "scg = StochasticConformerGenerator(\n",
     "    smiles=smi,\n",
@@ -250,16 +304,25 @@
     "print(\n",
     "    f\"Number of conformers: {len(unique_mol_data)}\\n\"\n",
     "    f\"Metric: {scg.metric.metric_history[-1]:.3e}\"\n",
-    ")"
+    ")\n",
+    "mol = dict_to_mol(unique_mol_data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5aee01ec",
+   "id": "d9e4925b",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from rdkit import Chem\n",
+    "\n",
+    "Chem.rdMolAlign.AlignMolConformers(\n",
+    "    mol, atomIds=[atom.GetIdx() for atom in mol.GetHeavyAtoms()]\n",
+    ")  # Align heavy atoms\n",
+    "\n",
+    "interactive_conformer_viewer(mol)"
+   ]
   }
  ],
  "metadata": {
diff --git a/ipython/Forcefield Examples.ipynb b/ipython/Forcefield Examples.ipynb
index f15494dc..ffa00f86 100644
--- a/ipython/Forcefield Examples.ipynb	
+++ b/ipython/Forcefield Examples.ipynb	
@@ -16,18 +16,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "import sys\n",
@@ -54,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,17 +62,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mmff94s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = RDKitFF('mmff94s')\n",
     "print(ff.type)"
@@ -96,17 +79,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimizability of this molecule: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "opt_mol = mol1.Copy()\n",
     "ff.setup(opt_mol, ignore_interfrag_interactions=False)\n",
@@ -122,83 +97,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimization success: True\n"
-     ]
-    },
-    {
-     "data": {
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-       "viewer_1711054440895725.zoomTo();\n",
-       "\tviewer_1711054440895725.addModel(\"\\n     RDKit          3D\\n\\n 12 11  0  0  0  0  0  0  0  0999 V2000\\n   -1.5426   -0.3102   -0.0613 C   0  0  0  0  0  0  0  0  0  1  0  0\\n   -0.1266   -0.6610    0.3586 C   0  0  0  0  0  0  0  0  0  2  0  0\\n    0.8060    0.5357    0.2579 C   0  0  0  0  0  0  0  0  0  3  0  0\\n    2.1090    0.1405    0.6634 O   0  0  0  0  0  0  0  0  0  4  0  0\\n    3.0104    1.2312    0.5837 O   0  0  0  0  0  1  0  0  0  5  0  0\\n   -2.1912   -1.1879    0.0208 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -1.5695    0.0361   -1.0994 H   0  0  0  0  0  0  0  0  0  7  0  0\\n   -1.9563    0.4786    0.5751 H   0  0  0  0  0  0  0  0  0  8  0  0\\n   -0.1268   -1.0419    1.3869 H   0  0  0  0  0  0  0  0  0  9  0  0\\n    0.2549   -1.4784   -0.2649 H   0  0  0  0  0  0  0  0  0 10  0  0\\n    0.8603    0.9068   -0.7704 H   0  0  0  0  0  0  0  0  0 11  0  0\\n    0.4724    1.3504    0.9082 H   0  0  0  0  0  0  0  0  0 12  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  1  0\\n  1  6  1  0\\n  1  7  1  0\\n  1  8  1  0\\n  2  9  1  0\\n  2 10  1  0\\n  3 11  1  0\\n  3 12  1  0\\nM  RAD  1   5   2\\nM  END\\n\",\"sdf\");\n",
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-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x177a08dd0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "success = ff.optimize()\n",
     "print(f'Optimization success: {success}')\n",
@@ -222,17 +123,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gaff\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "obff = OpenBabelFF('gaff')\n",
     "print(obff.type)"
@@ -247,17 +140,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimizability of this molecule: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "opt_mol = mol1.Copy()\n",
     "obff.setup(opt_mol)\n",
@@ -266,76 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/html": [
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-       "        </div>\n",
-       "<script>\n",
-       "\n",
-       "var loadScriptAsync = function(uri){\n",
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-       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
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-       "viewer_1711054549338419 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054549338419\"),{backgroundColor:\"white\"});\n",
-       "viewer_1711054549338419.zoomTo();\n",
-       "\tviewer_1711054549338419.addModel(\"\\n     RDKit          3D\\n\\n 12 11  0  0  0  0  0  0  0  0999 V2000\\n   -1.5544   -0.2992   -0.0767 C   0  0  0  0  0  0  0  0  0  1  0  0\\n   -0.1271   -0.6779    0.3572 C   0  0  0  0  0  0  0  0  0  2  0  0\\n    0.8203    0.5318    0.2699 C   0  0  0  0  0  0  0  0  0  3  0  0\\n    2.1405    0.1115    0.6693 O   0  0  0  0  0  0  0  0  0  4  0  0\\n    2.9671    1.3641    0.5514 O   0  0  0  0  0  1  0  0  0  5  0  0\\n   -2.2190   -1.1608    0.0171 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -1.5652    0.0313   -1.1183 H   0  0  0  0  0  0  0  0  0  7  0  0\\n   -1.9466    0.5065    0.5490 H   0  0  0  0  0  0  0  0  0  8  0  0\\n   -0.1464   -1.0531    1.3833 H   0  0  0  0  0  0  0  0  0  9  0  0\\n    0.2452   -1.4827   -0.2818 H   0  0  0  0  0  0  0  0  0 10  0  0\\n    0.8604    0.9078   -0.7567 H   0  0  0  0  0  0  0  0  0 11  0  0\\n    0.4799    1.3266    0.9400 H   0  0  0  0  0  0  0  0  0 12  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  1  0\\n  1  6  1  0\\n  1  7  1  0\\n  1  8  1  0\\n  2  9  1  0\\n  2 10  1  0\\n  3 11  1  0\\n  3 12  1  0\\nM  RAD  1   5   2\\nM  END\\n\",\"sdf\");\n",
-       "\tviewer_1711054549338419.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewer_1711054549338419.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewer_1711054549338419.zoomTo();\n",
-       "viewer_1711054549338419.render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x11ca52990>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "obff.optimize()\n",
     "opt_mol = obff.get_optimized_mol()\n",
@@ -359,76 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_1711054624873312\"  style=\"position: relative; width: 400px; height: 400px;\">\n        <p id=\"3dmolwarning_1711054624873312\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n        </div>\n<script>\n\nvar loadScriptAsync = function(uri){\n  return new Promise((resolve, reject) => {\n    //this is to ignore the existence of requirejs amd\n    var savedexports, savedmodule;\n    if (typeof exports !== 'undefined') savedexports = exports;\n    else exports = {}\n    if (typeof module !== 'undefined') savedmodule = module;\n    else module = {}\n\n    var tag = document.createElement('script');\n    tag.src = uri;\n    tag.async = true;\n    tag.onload = () => {\n        exports = savedexports;\n        module = savedmodule;\n        resolve();\n    };\n  var firstScriptTag = document.getElementsByTagName('script')[0];\n  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n});\n};\n\nif(typeof $3Dmolpromise === 'undefined') {\n$3Dmolpromise = null;\n  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n}\n\nvar viewer_1711054624873312 = null;\nvar warn = document.getElementById(\"3dmolwarning_1711054624873312\");\nif(warn) {\n    warn.parentNode.removeChild(warn);\n}\n$3Dmolpromise.then(function() {\nviewer_1711054624873312 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054624873312\"),{backgroundColor:\"white\"});\nviewer_1711054624873312.zoomTo();\n\tviewer_1711054624873312.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n   -0.0321    0.0069    0.0046 C   0  0  0  0  0  0  0  0  0  1  0  0\\n    1.1659   -0.0246   -0.1596 O   0  0  0  0  0  0  0  0  0  2  0  0\\n   -0.0335   -0.0115    0.0033 C   0  0  0  0  0  0  0  0  0  3  0  0\\n    1.1681   -0.1635   -0.0898 O   0  0  0  0  0  0  0  0  0  4  0  0\\n   -0.5753   -0.9283    0.0474 H   0  0  0  0  0  0  0  0  0  5  0  0\\n   -0.5585    0.9461    0.1076 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -0.7164   -0.8252    0.0944 H   0  0  0  0  0  0  0  0  0  7  0  0\\n   -0.4182    1.0002   -0.0079 H   0  0  0  0  0  0  0  0  0  8  0  0\\n  1  2  2  0\\n  3  4  2  0\\n  1  5  1  0\\n  1  6  1  0\\n  3  7  1  0\\n  3  8  1  0\\nM  END\\n\",\"sdf\");\n\tviewer_1711054624873312.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n\tviewer_1711054624873312.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n\tviewer_1711054624873312.zoomTo();\nviewer_1711054624873312.render();\n});\n</script>",
-      "text/html": [
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-       "        </div>\n",
-       "<script>\n",
-       "\n",
-       "var loadScriptAsync = function(uri){\n",
-       "  return new Promise((resolve, reject) => {\n",
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-       "$3Dmolpromise = null;\n",
-       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
-       "}\n",
-       "\n",
-       "var viewer_1711054624873312 = null;\n",
-       "var warn = document.getElementById(\"3dmolwarning_1711054624873312\");\n",
-       "if(warn) {\n",
-       "    warn.parentNode.removeChild(warn);\n",
-       "}\n",
-       "$3Dmolpromise.then(function() {\n",
-       "viewer_1711054624873312 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054624873312\"),{backgroundColor:\"white\"});\n",
-       "viewer_1711054624873312.zoomTo();\n",
-       "\tviewer_1711054624873312.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n   -0.0321    0.0069    0.0046 C   0  0  0  0  0  0  0  0  0  1  0  0\\n    1.1659   -0.0246   -0.1596 O   0  0  0  0  0  0  0  0  0  2  0  0\\n   -0.0335   -0.0115    0.0033 C   0  0  0  0  0  0  0  0  0  3  0  0\\n    1.1681   -0.1635   -0.0898 O   0  0  0  0  0  0  0  0  0  4  0  0\\n   -0.5753   -0.9283    0.0474 H   0  0  0  0  0  0  0  0  0  5  0  0\\n   -0.5585    0.9461    0.1076 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -0.7164   -0.8252    0.0944 H   0  0  0  0  0  0  0  0  0  7  0  0\\n   -0.4182    1.0002   -0.0079 H   0  0  0  0  0  0  0  0  0  8  0  0\\n  1  2  2  0\\n  3  4  2  0\\n  1  5  1  0\\n  1  6  1  0\\n  3  7  1  0\\n  3  8  1  0\\nM  END\\n\",\"sdf\");\n",
-       "\tviewer_1711054624873312.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewer_1711054624873312.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewer_1711054624873312.zoomTo();\n",
-       "viewer_1711054624873312.render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17a3c0dd0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mol2 = Mol.FromSmiles('C=O.C=O')\n",
     "mol2.EmbedConformer()\n",
@@ -444,76 +195,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_171105478107425\"  style=\"position: relative; width: 400px; height: 400px;\">\n        <p id=\"3dmolwarning_171105478107425\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n        </div>\n<script>\n\nvar loadScriptAsync = function(uri){\n  return new Promise((resolve, reject) => {\n    //this is to ignore the existence of requirejs amd\n    var savedexports, savedmodule;\n    if (typeof exports !== 'undefined') savedexports = exports;\n    else exports = {}\n    if (typeof module !== 'undefined') savedmodule = module;\n    else module = {}\n\n    var tag = document.createElement('script');\n    tag.src = uri;\n    tag.async = true;\n    tag.onload = () => {\n        exports = savedexports;\n        module = savedmodule;\n        resolve();\n    };\n  var firstScriptTag = document.getElementsByTagName('script')[0];\n  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n});\n};\n\nif(typeof $3Dmolpromise === 'undefined') {\n$3Dmolpromise = null;\n  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n}\n\nvar viewer_171105478107425 = null;\nvar warn = document.getElementById(\"3dmolwarning_171105478107425\");\nif(warn) {\n    warn.parentNode.removeChild(warn);\n}\n$3Dmolpromise.then(function() {\nviewer_171105478107425 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_171105478107425\"),{backgroundColor:\"white\"});\nviewer_171105478107425.zoomTo();\n\tviewer_171105478107425.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    0.3013    1.4798    0.6965 C   0  0  0  0  0  0  0  0  0  1  0  0\\n    0.7048    0.5731    1.4175 O   0  0  0  0  0  0  0  0  0  2  0  0\\n   -0.3016   -1.4798   -0.6963 C   0  0  0  0  0  0  0  0  0  3  0  0\\n   -0.7513   -0.5744   -1.3911 O   0  0  0  0  0  0  0  0  0  4  0  0\\n    0.9872    2.1315    0.1322 H   0  0  0  0  0  0  0  0  0  5  0  0\\n   -0.7718    1.6926    0.5644 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -0.7595   -1.7667    0.2641 H   0  0  0  0  0  0  0  0  0  7  0  0\\n    0.5910   -2.0561   -0.9873 H   0  0  0  0  0  0  0  0  0  8  0  0\\n  1  2  2  0\\n  3  4  2  0\\n  1  5  1  0\\n  1  6  1  0\\n  3  7  1  0\\n  3  8  1  0\\nM  END\\n\",\"sdf\");\n\tviewer_171105478107425.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n\tviewer_171105478107425.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n\tviewer_171105478107425.zoomTo();\nviewer_171105478107425.render();\n});\n</script>",
-      "text/html": [
-       "<div id=\"3dmolviewer_171105478107425\"  style=\"position: relative; width: 400px; height: 400px;\">\n",
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-       "        </div>\n",
-       "<script>\n",
-       "\n",
-       "var loadScriptAsync = function(uri){\n",
-       "  return new Promise((resolve, reject) => {\n",
-       "    //this is to ignore the existence of requirejs amd\n",
-       "    var savedexports, savedmodule;\n",
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-       "    else exports = {}\n",
-       "    if (typeof module !== 'undefined') savedmodule = module;\n",
-       "    else module = {}\n",
-       "\n",
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-       "    tag.src = uri;\n",
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-       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
-       "});\n",
-       "};\n",
-       "\n",
-       "if(typeof $3Dmolpromise === 'undefined') {\n",
-       "$3Dmolpromise = null;\n",
-       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
-       "}\n",
-       "\n",
-       "var viewer_171105478107425 = null;\n",
-       "var warn = document.getElementById(\"3dmolwarning_171105478107425\");\n",
-       "if(warn) {\n",
-       "    warn.parentNode.removeChild(warn);\n",
-       "}\n",
-       "$3Dmolpromise.then(function() {\n",
-       "viewer_171105478107425 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_171105478107425\"),{backgroundColor:\"white\"});\n",
-       "viewer_171105478107425.zoomTo();\n",
-       "\tviewer_171105478107425.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    0.3013    1.4798    0.6965 C   0  0  0  0  0  0  0  0  0  1  0  0\\n    0.7048    0.5731    1.4175 O   0  0  0  0  0  0  0  0  0  2  0  0\\n   -0.3016   -1.4798   -0.6963 C   0  0  0  0  0  0  0  0  0  3  0  0\\n   -0.7513   -0.5744   -1.3911 O   0  0  0  0  0  0  0  0  0  4  0  0\\n    0.9872    2.1315    0.1322 H   0  0  0  0  0  0  0  0  0  5  0  0\\n   -0.7718    1.6926    0.5644 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -0.7595   -1.7667    0.2641 H   0  0  0  0  0  0  0  0  0  7  0  0\\n    0.5910   -2.0561   -0.9873 H   0  0  0  0  0  0  0  0  0  8  0  0\\n  1  2  2  0\\n  3  4  2  0\\n  1  5  1  0\\n  1  6  1  0\\n  3  7  1  0\\n  3  8  1  0\\nM  END\\n\",\"sdf\");\n",
-       "\tviewer_171105478107425.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewer_171105478107425.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewer_171105478107425.zoomTo();\n",
-       "viewer_171105478107425.render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17cbc0790>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = RDKitFF()\n",
     "ff.setup(mol2.Copy(), ignore_interfrag_interactions=False)\n",
@@ -530,76 +214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "        </div>\n",
-       "<script>\n",
-       "\n",
-       "var loadScriptAsync = function(uri){\n",
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-       "$3Dmolpromise = null;\n",
-       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
-       "}\n",
-       "\n",
-       "var viewer_1711054795899416 = null;\n",
-       "var warn = document.getElementById(\"3dmolwarning_1711054795899416\");\n",
-       "if(warn) {\n",
-       "    warn.parentNode.removeChild(warn);\n",
-       "}\n",
-       "$3Dmolpromise.then(function() {\n",
-       "viewer_1711054795899416 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054795899416\"),{backgroundColor:\"white\"});\n",
-       "viewer_1711054795899416.zoomTo();\n",
-       "\tviewer_1711054795899416.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n   -0.4446    0.0731    1.4457 C   0  0  0  0  0  0  0  0  0  1  0  0\\n   -0.2504    1.0090    0.6773 O   0  0  0  0  0  0  0  0  0  2  0  0\\n    0.2699   -0.4378   -1.8636 C   0  0  0  0  0  0  0  0  0  3  0  0\\n    0.4429   -1.5947   -1.4967 O   0  0  0  0  0  0  0  0  0  4  0  0\\n    0.2492   -0.7796    1.5246 H   0  0  0  0  0  0  0  0  0  5  0  0\\n   -1.3292    0.0279    2.1010 H   0  0  0  0  0  0  0  0  0  6  0  0\\n   -0.7174   -0.0625   -2.1751 H   0  0  0  0  0  0  0  0  0  7  0  0\\n    1.0885    0.3008   -1.8927 H   0  0  0  0  0  0  0  0  0  8  0  0\\n  1  2  2  0\\n  3  4  2  0\\n  1  5  1  0\\n  1  6  1  0\\n  3  7  1  0\\n  3  8  1  0\\nM  END\\n\",\"sdf\");\n",
-       "\tviewer_1711054795899416.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewer_1711054795899416.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewer_1711054795899416.zoomTo();\n",
-       "viewer_1711054795899416.render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x109902810>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = OpenBabelFF()\n",
     "ff.setup(mol2.Copy())\n",
@@ -623,86 +240,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before optimization\n",
-      "d(1,2) = 3.091533633597571\n",
-      "d(0,3) = 5.54344245067431\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_1711054956206383\"  style=\"position: relative; width: 400px; height: 400px;\">\n        <p id=\"3dmolwarning_1711054956206383\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n        </div>\n<script>\n\nvar loadScriptAsync = function(uri){\n  return new Promise((resolve, reject) => {\n    //this is to ignore the existence of requirejs amd\n    var savedexports, savedmodule;\n    if (typeof exports !== 'undefined') savedexports = exports;\n    else exports = {}\n    if (typeof module !== 'undefined') savedmodule = module;\n    else module = {}\n\n    var tag = document.createElement('script');\n    tag.src = uri;\n    tag.async = true;\n    tag.onload = () => {\n        exports = savedexports;\n        module = savedmodule;\n        resolve();\n    };\n  var firstScriptTag = document.getElementsByTagName('script')[0];\n  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n});\n};\n\nif(typeof $3Dmolpromise === 'undefined') {\n$3Dmolpromise = null;\n  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n}\n\nvar viewer_1711054956206383 = null;\nvar warn = document.getElementById(\"3dmolwarning_1711054956206383\");\nif(warn) {\n    warn.parentNode.removeChild(warn);\n}\n$3Dmolpromise.then(function() {\nviewer_1711054956206383 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054956206383\"),{backgroundColor:\"white\"});\nviewer_1711054956206383.zoomTo();\n\tviewer_1711054956206383.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    2.1642    0.1807    0.0054 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.9426    0.0787    0.0023 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1382   -0.1785   -0.0053 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.3600   -0.2805   -0.0084 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8265   -0.6920    0.1238 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.6732    1.1511   -0.1101 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5187   -0.5296   -0.8456 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5896    0.2701    0.8379 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  7  3  1  0\\n  6  1  1  0\\n  4  3  2  0\\n  3  8  1  0\\n  2  1  2  0\\n  1  5  1  0\\nM  END\\n\",\"sdf\");\n\tviewer_1711054956206383.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n\tviewer_1711054956206383.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n\tviewer_1711054956206383.zoomTo();\nviewer_1711054956206383.render();\n});\n</script>",
-      "text/html": [
-       "<div id=\"3dmolviewer_1711054956206383\"  style=\"position: relative; width: 400px; height: 400px;\">\n",
-       "        <p id=\"3dmolwarning_1711054956206383\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
-       "        </div>\n",
-       "<script>\n",
-       "\n",
-       "var loadScriptAsync = function(uri){\n",
-       "  return new Promise((resolve, reject) => {\n",
-       "    //this is to ignore the existence of requirejs amd\n",
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-       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
-       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
-       "});\n",
-       "};\n",
-       "\n",
-       "if(typeof $3Dmolpromise === 'undefined') {\n",
-       "$3Dmolpromise = null;\n",
-       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
-       "}\n",
-       "\n",
-       "var viewer_1711054956206383 = null;\n",
-       "var warn = document.getElementById(\"3dmolwarning_1711054956206383\");\n",
-       "if(warn) {\n",
-       "    warn.parentNode.removeChild(warn);\n",
-       "}\n",
-       "$3Dmolpromise.then(function() {\n",
-       "viewer_1711054956206383 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054956206383\"),{backgroundColor:\"white\"});\n",
-       "viewer_1711054956206383.zoomTo();\n",
-       "\tviewer_1711054956206383.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    2.1642    0.1807    0.0054 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.9426    0.0787    0.0023 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1382   -0.1785   -0.0053 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.3600   -0.2805   -0.0084 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8265   -0.6920    0.1238 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.6732    1.1511   -0.1101 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5187   -0.5296   -0.8456 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5896    0.2701    0.8379 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  7  3  1  0\\n  6  1  1  0\\n  4  3  2  0\\n  3  8  1  0\\n  2  1  2  0\\n  1  5  1  0\\nM  END\\n\",\"sdf\");\n",
-       "\tviewer_1711054956206383.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewer_1711054956206383.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewer_1711054956206383.zoomTo();\n",
-       "viewer_1711054956206383.render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17a37f7d0>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xyz = \"\"\"C      2.164196    0.180658    0.005381\n",
     "O      0.942631    0.078687    0.002344\n",
@@ -732,86 +272,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "After Optimization:\n",
-      "d(1,2) = 2.0104161144672386\n",
-      "d(0,3) = 2.0104161207438698\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_1711054951660681\"  style=\"position: relative; width: 400px; height: 400px;\">\n        <p id=\"3dmolwarning_1711054951660681\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n        </div>\n<script>\n\nvar loadScriptAsync = function(uri){\n  return new Promise((resolve, reject) => {\n    //this is to ignore the existence of requirejs amd\n    var savedexports, savedmodule;\n    if (typeof exports !== 'undefined') savedexports = exports;\n    else exports = {}\n    if (typeof module !== 'undefined') savedmodule = module;\n    else module = {}\n\n    var tag = document.createElement('script');\n    tag.src = uri;\n    tag.async = true;\n    tag.onload = () => {\n        exports = savedexports;\n        module = savedmodule;\n        resolve();\n    };\n  var firstScriptTag = document.getElementsByTagName('script')[0];\n  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n});\n};\n\nif(typeof $3Dmolpromise === 'undefined') {\n$3Dmolpromise = null;\n  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n}\n\nvar viewer_1711054951660681 = null;\nvar warn = document.getElementById(\"3dmolwarning_1711054951660681\");\nif(warn) {\n    warn.parentNode.removeChild(warn);\n}\n$3Dmolpromise.then(function() {\nviewer_1711054951660681 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1711054951660681\"),{backgroundColor:\"white\"});\nviewer_1711054951660681.zoomTo();\n\tviewer_1711054951660681.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    1.2067    0.2472    0.0576 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.4954   -0.1155    1.0041 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.2067   -0.2472   -0.0576 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4955    0.1157   -1.0041 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.7693   -0.4731   -0.5636 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3844    1.3145   -0.1677 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.3842   -1.3145    0.1677 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.7695    0.4730    0.5635 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  7  3  1  0\\n  6  1  1  0\\n  4  3  2  0\\n  3  8  1  0\\n  2  1  2  0\\n  1  5  1  0\\nM  END\\n\",\"sdf\");\n\tviewer_1711054951660681.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n\tviewer_1711054951660681.addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n\tviewer_1711054951660681.zoomTo();\nviewer_1711054951660681.render();\n});\n</script>",
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-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17cbe1d10>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = RDKitFF()\n",
     "ff.setup(mol3.Copy())\n",
@@ -836,85 +299,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d(1,2) = 2.0010775081854573\n",
-      "d(0,3) = 2.000991178438568\n",
-      "\n"
-     ]
-    },
-    {
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-       "\tviewer_1711055010551869.addModel(\"\\n     RDKit          3D\\n\\n  8  6  0  0  0  0  0  0  0  0999 V2000\\n    0.9775    0.1991   -0.1045 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.3599   -0.8289   -0.4137 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4573   -0.1213    0.0353 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.8539    0.9487    0.1919 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.4507    0.3011    0.8882 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.1896    1.0170   -0.8188 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.8577   -0.4515   -0.9415 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.7183   -0.7605    0.8975 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  7  3  1  0\\n  6  1  1  0\\n  4  3  2  0\\n  3  8  1  0\\n  2  1  2  0\\n  1  5  1  0\\nM  END\\n\",\"sdf\");\n",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17cbc2910>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = OpenBabelFF()\n",
     "ff.setup(mol3.Copy())  # directly assign mol to allow correct atom index when setting constraints\n",
@@ -939,114 +326,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "conformer 0\n",
-      "d(1,2) = 1.2175480914480463\n",
-      "d(0,3) = 1.2141275783373007\n",
-      "\n",
-      "conformer 1\n",
-      "d(1,2) = 1.1921424503791371\n",
-      "d(0,3) = 1.2442288478856651\n",
-      "\n",
-      "conformer 2\n",
-      "d(1,2) = 1.205391551161533\n",
-      "d(0,3) = 1.237817705103411\n",
-      "\n",
-      "conformer 3\n",
-      "d(1,2) = 1.196583728687554\n",
-      "d(0,3) = 1.2153392084098922\n",
-      "\n"
-     ]
-    },
-    {
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17767d190>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xyz = \"\"\"C      2.164196    0.180658    0.005381\n",
     "O      0.942631    0.078687    0.002344\n",
@@ -1081,115 +363,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "conformer 0\n",
-      "d(1,2) = 2.0813138368027926\n",
-      "d(0,3) = 2.0813176199831016\n",
-      "\n",
-      "conformer 1\n",
-      "d(1,2) = 2.0813154376358542\n",
-      "d(0,3) = 2.0813153060303526\n",
-      "\n",
-      "conformer 2\n",
-      "d(1,2) = 2.0813155343734144\n",
-      "d(0,3) = 2.08131529926818\n",
-      "\n",
-      "conformer 3\n",
-      "d(1,2) = 2.0813154240425233\n",
-      "d(0,3) = 2.0813157149607884\n",
-      "\n",
-      "Force field energies for each conformers: [44.31933079730308, 44.319330840996656, 44.3193308404387, 44.319330795469625]\n"
-     ]
-    },
-    {
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-       "\tviewergrid_17110551877644842[1][1].setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewergrid_17110551877644842[1][1].addPropertyLabels(\"index\",{},{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
-       "\tviewergrid_17110551877644842[1][1].zoomTo();\n",
-       "viewergrid_17110551877644842[1][1].render();\n",
-       "viewergrid_17110551877644842[1][0].render();\n",
-       "viewergrid_17110551877644842[0][1].render();\n",
-       "viewergrid_17110551877644842[0][0].render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x17ed73f10>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ff = RDKitFF()\n",
     "ff.setup(mol5.Copy())\n",
@@ -1218,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1242,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1254,30 +430,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x17604a390>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 1)\n",
     "plot_curve(x=angles1, y=rigid_E,\n",
@@ -1295,86 +450,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-1.177187    0.956920    1.239853\\nH      0.258382    0.245476    0.494181\\nH      0.872814    0.940329   -1.990098\\nH     -0.305757    1.896713   -2.896317\\nH     -0.668801    0.219709   -2.455133\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -0.982652    1.674248   -0.707897\\nC     -0.775609    0.545695    0.302921\\nC     -0.504720    1.318538   -2.117535\\nH     -0.460338    2.566796   -0.342684\\nH     -2.049331    1.935587   -0.731320\\nH     -1.075960    0.879926    1.306061\\nH      0.282556    0.274140    0.351862\\nH      0.576667    1.150629   -2.133546\\nH     -0.729979    2.116864   -2.829247\\nH     -0.986100    0.402139   -2.464682\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.135751    1.728032   -0.597429\\nC     -0.775609    0.545695    0.302921\\nC     -0.817316    1.482520   -2.074029\\nH     -0.601767    2.614837   -0.235113\\nH     -2.205279    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-1.020706    2.675983    0.356298\\nH     -2.501423    1.782167    0.644280\\nH     -0.472817    0.522701    1.359330\\nH      0.137388    0.549954   -0.298966\\nH     -1.095967    2.080960   -2.078529\\nH     -2.627507    2.827103   -1.606735\\nH     -2.514577    1.069733   -1.800865\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.662792    1.787354    0.205751\\nC     -0.775609    0.545695    0.302921\\nC     -2.226364    2.019383   -1.198021\\nH     -1.078534    2.657025    0.530016\\nH     -2.486327    1.689766    0.926111\\nH     -0.349287    0.466577    1.312971\\nH      0.059119    0.624069   -0.399173\\nH     -1.423116    2.217342   -1.914578\\nH     -2.910387    2.871618   -1.218243\\nH     -2.766854    1.136077   -1.543950\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.706092    1.755676    0.394581\\nC     -0.775609    0.545695    0.302921\\nC     -2.438841    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-0.775609    0.545695    0.302921\\nC     -2.917871    1.935097    0.400649\\nH     -1.036209    2.378002    1.387259\\nH     -1.899007    1.015427    2.075250\\nH      0.154192    0.321029    0.844406\\nH     -0.496874    1.012211   -0.646036\\nH     -2.681191    2.508765   -0.500871\\nH     -3.546989    2.560159    1.039482\\nH     -3.498361    1.062815    0.093764\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.566940    1.412570    1.283010\\nC     -0.775609    0.545695    0.302921\\nC     -2.933231    1.841775    0.743510\\nH     -0.962609    2.292985    1.532986\\nH     -1.695242    0.856541    2.221637\\nH      0.218281    0.322915    0.716157\\nH     -0.625792    1.082733   -0.637821\\nH     -2.822855    2.480980   -0.138013\\nH     -3.501334    2.399581    1.492316\\nH     -3.518675    0.969170    0.447131\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.471787    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-1.456032   -0.656478   -0.054778\\nC     -0.923940    0.899848    1.783065\\nC     -0.775609    0.545695    0.302921\\nC     -2.379328    1.090048    2.216306\\nH     -0.345599    1.810392    1.981219\\nH     -0.457163    0.107519    2.383886\\nH      0.288924    0.485360    0.035826\\nH     -1.224689    1.326354   -0.317360\\nH     -2.833854    1.940650    1.698878\\nH     -2.455745    1.272529    3.291229\\nH     -2.969146    0.203532    1.975003\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -0.758525    0.797907    1.811016\\nC     -0.775609    0.545695    0.302921\\nC     -2.157559    0.903244    2.422272\\nH     -0.189274    1.715574    2.002522\\nH     -0.199566   -0.012712    2.297959\\nH      0.251161    0.544007   -0.089446\\nH     -1.318946    1.346616   -0.206158\\nH     -2.696288    1.767085    2.020526\\nH     -2.111768    1.013118    3.508748\\nH     -2.744055    0.011867    2.190986\\nH     -1.578000   -0.649350   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1.270960\\nH      0.846153   -0.369401    2.462424\\nH      2.228691   -0.835199    1.464147\\nH      0.663225   -1.636156    1.248341\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.746256    0.417468    0.227187\\nC     -0.775609    0.545695    0.302921\\nC      1.306998   -0.675949    1.139347\\nH      1.188908    1.388476    0.480242\\nH      1.031951    0.216268   -0.814230\\nH     -1.113484    1.385263   -0.320920\\nH     -1.084532    0.756721    1.330484\\nH      1.118024   -0.442771    2.191852\\nH      2.386384   -0.789417    1.010314\\nH      0.831947   -1.634970    0.923407\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.729818    0.481744    0.042434\\nC     -0.775609    0.545695    0.302921\\nC      1.428228   -0.645489    0.806369\\nH      1.169027    1.450910    0.308230\\nH      0.896278    0.359169   -1.036416\\nH     -1.210824    1.409901   -0.218565\\nH     -0.967068    0.678059    1.371412\\nH      1.358083   -0.487986    1.887166\\nH      2.487734   -0.708462    0.545445\\nH      0.960887   -1.606345    0.581931\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.689634    0.557082   -0.134328\\nC     -0.775609    0.545695    0.302921\\nC      1.508173   -0.588998    0.464438\\nH      1.126992    1.523458    0.144366\\nH      0.730888    0.510690   -1.231049\\nH     -1.296074    1.424111   -0.104158\\nH     -0.843204    0.601544    1.392949\\nH      1.561658   -0.504165    1.554297\\nH      2.530766   -0.593908    0.078590\\nH      1.047534   -1.550838    0.230560\\nH     -1.578000   -0.649350   -1.009564\\n\",\"xyz\");\n\tviewergrid_1711055601674338[0][0].setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n\tviewergrid_1711055601674338[0][0].animate({\"loop\": \"forward\", \"reps\": 0, \"step\": 1, \"interval\": 120});\n\tviewergrid_1711055601674338[0][0].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n\tviewergrid_1711055601674338[0][1].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n\tviewergrid_1711055601674338[0][0].zoomTo();\n\tviewergrid_1711055601674338[0][1].addModelsAsFrames(\"12\\n\\nO     -1.430607   -0.643928   -0.065245\\nC      0.614629    0.632252   -0.310563\\nC     -0.763861    0.546265    0.331805\\nC      1.528625   -0.503959    0.125286\\nH      1.078230    1.589740   -0.048792\\nH      0.515190    0.597806   -1.401663\\nH     -1.380179    1.401181    0.036810\\nH     -0.685807    0.545485    1.423549\\nH      1.667038   -0.500240    1.210977\\nH      2.512567   -0.397385   -0.342589\\nH      1.121131   -1.476953   -0.166516\\nH     -1.565503   -0.595594   -1.026984\\n12\\n\\nO     -1.433551   -0.644110   -0.065775\\nC      0.530800    0.727199   -0.457217\\nC     -0.760833    0.543563    0.329679\\nC      1.522704   -0.400812   -0.213531\\nH      0.992580    1.681420   -0.180334\\nH      0.306047    0.765792   -1.529578\\nH     -1.433967    1.389638    0.160922\\nH     -0.559140    0.480576    1.403609\\nH      1.798447   -0.456556    0.844340\\nH      2.435640   -0.232263   -0.793656\\nH      1.107992   -1.368377   -0.512643\\nH     -1.569330   -0.598430   -1.027382\\n12\\n\\nO     -1.440771   -0.640191   -0.062085\\nC      0.425602    0.825169   -0.592217\\nC     -0.755140    0.541432    0.328809\\nC      1.475318   -0.275470   -0.540757\\nH      0.886309    1.777831   -0.308488\\nH      0.073355    0.921100   -1.625772\\nH     -1.467099    1.372172    0.296638\\nH     -0.424979    0.418745    1.364970\\nH      1.866090   -0.396053    0.474654\\nH      2.313636   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-0.264101\\nH      1.898643    0.428606   -1.929344\\nH      0.833534   -0.914860   -1.489243\\nH     -1.610979   -0.605668   -1.015524\\n12\\n\\nO     -1.460874   -0.636112   -0.054825\\nC      0.013686    1.139321   -0.859918\\nC     -0.745710    0.535317    0.316613\\nC      1.089320    0.205086   -1.394766\\nH      0.477784    2.080771   -0.546394\\nH     -0.685316    1.368311   -1.672606\\nH     -1.471957    1.259726    0.698114\\nH     -0.065946    0.276992    1.134764\\nH      1.826965   -0.025857   -0.619293\\nH      1.614024    0.674916   -2.232751\\nH      0.660607   -0.736521   -1.752058\\nH     -1.625756   -0.609163   -1.011810\\n12\\n\\nO     -1.475400   -0.629967   -0.045010\\nC     -0.150757    1.239881   -0.904885\\nC     -0.741122    0.533948    0.311366\\nC      0.891403    0.389614   -1.617546\\nH      0.311064    2.181204   -0.587499\\nH     -0.948743    1.483971   -1.615863\\nH     -1.427241    1.209392    0.831798\\nH      0.042900    0.241823    1.016855\\nH      1.716989    0.137594   -0.944110\\nH      1.305166    0.936832   -2.470611\\nH      0.458859   -0.542371   -1.993969\\nH     -1.656807   -0.608390   -0.999371\\n12\\n\\nO     -1.486811   -0.627524   -0.032634\\nC     -0.323016    1.336180   -0.922591\\nC     -0.737684    0.532141    0.305736\\nC      0.663160    0.582132   -1.803465\\nH      0.135258    2.277527   -0.599369\\nH     -1.207946    1.584816   -1.519378\\nH     -1.365766    1.150196    0.955157\\nH      0.137043    0.218951    0.884375\\nH      1.568663    0.325151   -1.244574\\nH      0.956158    1.202063   -2.656830\\nH      0.226845   -0.342327   -2.193816\\nH     -1.683812   -0.612583   -0.983852\\n12\\n\\nO     -1.495960   -0.625978   -0.017400\\nC     -0.499788    1.426719   -0.913109\\nC     -0.734786    0.531889    0.299965\\nC      0.408788    0.777164   -1.947849\\nH     -0.044936    2.367443   -0.583178\\nH     -1.457729    1.667637   -1.388086\\nH     -1.286960    1.086286    1.065207\\nH      0.214200    0.208792    0.739233\\nH      1.381523    0.527613   -1.512067\\nH      0.578417    1.463675   -2.783508\\nH     -0.031623   -0.139658   -2.351195\\nH     -1.708874   -0.619715   -0.964707\\n12\\n\\nO     -1.496777   -0.628105   -0.000634\\nC     -0.675195    1.510185   -0.875650\\nC     -0.732696    0.533437    0.294979\\nC      0.129744    0.969853   -2.049834\\nH     -0.220015    2.447664   -0.538069\\nH     -1.690860    1.736756   -1.219898\\nH     -1.198978    1.019260    1.158183\\nH      0.272413    0.217153    0.591036\\nH      1.153828    0.731402   -1.744816\\nH      0.179798    1.718410   -2.847034\\nH     -0.322874    0.065996   -2.467653\\nH     -1.721171   -0.633390   -0.945198\\n12\\n\\nO     -1.494703   -0.631559    0.017383\\nC     -0.850701    1.583270   -0.814115\\nC     -0.731716    0.536320    0.289886\\nC     -0.162791    1.158300   -2.103985\\nH     -0.398963    2.519850   -0.467476\\nH     -1.908575    1.779217   -1.026274\\nH     -1.097366    0.951090    1.234350\\nH      0.312051    0.238642    0.436525\\nH      0.896020    0.942711   -1.930376\\nH     -0.226969    1.960451   -2.845288\\nH     -0.627707    0.267441   -2.535765\\nH     -1.729640   -0.648539   -0.924185\\n12\\n\\nO     -1.493745   -0.633721    0.034683\\nC     -1.016741    1.645105   -0.725245\\nC     -0.728221    0.538005    0.284296\\nC     -0.467887    1.338097   -2.111497\\nH     -0.563388    2.577374   -0.369988\\nH     -2.097694    1.806547   -0.800847\\nH     -0.977718    0.881053    1.293536\\nH      0.332158    0.264879    0.274686\\nH      0.611678    1.160790   -2.075079\\nH     -0.649808    2.183594   -2.782488\\nH     -0.945860    0.456925   -2.549267\\nH     -1.742271   -0.659914   -0.903199\\n12\\n\\nO     -1.472338   -0.642252    0.036453\\nC     -1.169939    1.698663   -0.612091\\nC     -0.730891    0.544283    0.283634\\nC     -0.784947    1.499839   -2.071685\\nH     -0.705021    2.623704   -0.251634\\nH     -2.256517    1.824025   -0.548692\\nH     -0.882633    0.810453    1.334119\\nH      0.331979    0.322260    0.144379\\nH      0.292976    1.340998   -2.175168\\nH     -1.053809    2.387002   -2.654255\\nH     -1.303327    0.643729   -2.513675\\nH     -1.710184   -0.677012   -0.903888\\n12\\n\\nO     -1.456346   -0.647435    0.032711\\nC     -1.307830    1.739469   -0.477728\\nC     -0.732447    0.548466    0.282100\\nC     -1.100549    1.646087   -1.983231\\nH     -0.829033    2.656255   -0.115100\\nH     -2.382350    1.822052   -0.276477\\nH     -0.776318    0.738665    1.359349\\nH      0.315640    0.380797    0.014020\\nH     -0.040265    1.525590   -2.226908\\nH     -1.457421    2.560359   -2.468165\\nH     -1.651714    0.806053   -2.415646\\nH     -1.684699   -0.684191   -0.910221\\n12\\n\\nO     -1.445723   -0.649392    0.028370\\nC     -1.432049    1.765329   -0.325056\\nC     -0.730924    0.552772    0.277655\\nC     -1.405598    1.774272   -1.846901\\nH     -0.941922    2.676202    0.037820\\nH     -2.476457    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-1.170439    2.624598    0.728967\\nH     -2.473356    1.504078    1.153611\\nH     -0.219022    0.425130    1.222274\\nH      0.006051    0.743633   -0.504545\\nH     -1.702574    2.274873   -1.705180\\nH     -3.078957    2.914948   -0.790992\\nH     -3.042354    1.205532   -1.243911\\nH     -1.629330   -0.643523   -0.969329\\n12\\n\\nO     -1.444628   -0.633555   -0.015918\\nC     -1.731197    1.690983    0.586198\\nC     -0.748822    0.571008    0.271649\\nC     -2.601801    2.053115   -0.608411\\nH     -1.177052    2.579071    0.909721\\nH     -2.384767    1.385517    1.411532\\nH     -0.112211    0.384882    1.142212\\nH     -0.099012    0.824193   -0.572690\\nH     -1.988407    2.366340   -1.459200\\nH     -3.271183    2.879782   -0.350184\\nH     -3.220235    1.206657   -0.922697\\nH     -1.631824   -0.640713   -0.970152\\n12\\n\\nO     -1.444965   -0.632805   -0.014424\\nC     -1.724860    1.633163    0.773858\\nC     -0.753096    0.574531    0.271340\\nC     -2.741538    2.034889   -0.284994\\nH     -1.164404    2.519795    1.089702\\nH     -2.266067    1.251150    1.647214\\nH     -0.013107    0.355339    1.047369\\nH     -0.215778    0.907187   -0.623350\\nH     -2.243234    2.427809   -1.177027\\nH     -3.404224    2.814080    0.104841\\nH     -3.362425    1.184613   -0.584107\\nH     -1.631971   -0.641916   -0.968782\\n12\\n\\nO     -1.442678   -0.634182   -0.011402\\nC     -1.695033    1.563213    0.954932\\nC     -0.757344    0.577671    0.271714\\nC     -2.841880    1.991176    0.050532\\nH     -1.129175    2.449295    1.263223\\nH     -2.116811    1.105249    1.857068\\nH      0.078034    0.338839    0.937085\\nH     -0.344187    0.989390   -0.655450\\nH     -2.464823    2.459127   -0.864423\\nH     -3.477970    2.717661    0.566017\\nH     -3.466400    1.137710   -0.231677\\nH     -1.630145   -0.647183   -0.965367\\n12\\n\\nO     -1.444124   -0.634243   -0.011135\\nC     -1.640545    1.485180    1.124911\\nC     -0.761784    0.578722    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-0.768668    0.581701    0.279425\\nC     -2.889172    1.714644    1.064167\\nH     -0.893822    2.187652    1.704498\\nH     -1.513495    0.639623    2.299363\\nH      0.263641    0.345604    0.554759\\nH     -0.749344    1.207120   -0.619943\\nH     -2.892504    2.383702    0.197690\\nH     -3.351832    2.243828    1.903124\\nH     -3.511815    0.845415    0.829976\\nH     -1.613947   -0.656370   -0.964874\\n12\\n\\nO     -1.432096   -0.641615   -0.009585\\nC     -1.355046    1.197889    1.546874\\nC     -0.771725    0.581850    0.282585\\nC     -2.819209    1.580151    1.382405\\nH     -0.777841    2.088828    1.817072\\nH     -1.274154    0.483666    2.374497\\nH      0.293002    0.369072    0.417362\\nH     -0.882401    1.264530   -0.567068\\nH     -2.945800    2.302781    0.569849\\nH     -3.195750    2.036203    2.303605\\nH     -3.437775    0.704218    1.163328\\nH     -1.606671   -0.657826   -0.965923\\n12\\n\\nO     -1.427595   -0.644006   -0.010740\\nC     -1.223551    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-0.015409\\nC     -0.920794    0.883695    1.777545\\nC     -0.781407    0.582786    0.291369\\nC     -2.370788    1.084027    2.194981\\nH     -0.347603    1.784945    2.021095\\nH     -0.503606    0.054551    2.360761\\nH      0.274197    0.499055    0.015799\\nH     -1.237361    1.378751   -0.307034\\nH     -2.827628    1.908947    1.638985\\nH     -2.426552    1.322305    3.261898\\nH     -2.964150    0.181049    2.020434\\nH     -1.583966   -0.655868   -0.973792\\n12\\n\\nO     -1.416102   -0.651419   -0.017744\\nC     -0.755624    0.782255    1.803872\\nC     -0.785309    0.582370    0.294619\\nC     -2.151022    0.899795    2.400434\\nH     -0.185315    1.687433    2.039531\\nH     -0.246936   -0.065933    2.276582\\nH      0.233049    0.558214   -0.105210\\nH     -1.334775    1.394386   -0.193050\\nH     -2.694294    1.742819    1.961708\\nH     -2.086698    1.063180    3.480907\\nH     -2.734532   -0.010729    2.232044\\nH     -1.576903   -0.655798   -0.976687\\n12\\n\\nO     -1.412681   -0.653351   -0.021720\\nC     -0.585579    0.685648    1.803986\\nC     -0.788914    0.581888    0.298469\\nC     -1.902984    0.710434    2.566042\\nH     -0.019786    1.595466    2.032532\\nH      0.001940   -0.170628    2.155174\\nH      0.176641    0.622745   -0.215227\\nH     -1.416962    1.400301   -0.068181\\nH     -2.521088    1.557328    2.251231\\nH     -1.715564    0.808139    3.640069\\nH     -2.473278   -0.209961    2.406388\\nH     -1.570519   -0.653167   -0.981201\\n12\\n\\nO     -1.409142   -0.655458   -0.026049\\nC     -0.413899    0.596104    1.777717\\nC     -0.792443    0.581033    0.302815\\nC     -1.631236    0.520672    2.688510\\nH      0.146384    1.510967    1.999813\\nH      0.237866   -0.257282    1.998549\\nH      0.105407    0.692495   -0.313429\\nH     -1.484991    1.394840    0.065334\\nH     -2.308155    1.361603    2.506867\\nH     -1.319957    0.556071    3.737306\\nH     -2.187773   -0.408944    2.533711\\nH     -1.564484   -0.650724   -0.985891\\n12\\n\\nO     -1.407998   -0.656259   -0.030722\\nC     -0.243714    0.514166    1.725627\\nC     -0.794763    0.579495    0.307606\\nC     -1.341048    0.334602    2.764982\\nH      0.311286    1.433431    1.942624\\nH      0.454972   -0.326289    1.810221\\nH      0.021961    0.763965   -0.397810\\nH     -1.534387    1.379725    0.205214\\nH     -2.060889    1.158027    2.720033\\nH     -0.908778    0.316472    3.770505\\nH     -1.882591   -0.604516    2.614631\\nH     -1.560726   -0.646529   -0.990964\\n12\\n\\nO     -1.405111   -0.658692   -0.034736\\nC     -0.078902    0.444557    1.648641\\nC     -0.796758    0.577194    0.312372\\nC     -1.038140    0.154947    2.794355\\nH      0.467574    1.369817    1.861402\\nH      0.652060   -0.370506    1.593829\\nH     -0.074126    0.838644   -0.467830\\nH     -1.568913    1.351904    0.348267\\nH     -1.779160    0.954400    2.895224\\nH     -0.487600    0.081669    3.737641\\nH     -1.569570   -0.789422    2.640660\\nH     -1.555501   -0.645562   -0.995230\\n12\\n\\nO     -1.405377   -0.658040   -0.040185\\nC      0.078537    0.383347    1.548528\\nC     -0.796594    0.574715    0.317245\\nC     -0.729135   -0.013841    2.775937\\nH      0.619263    1.312801    1.758334\\nH      0.821214   -0.399079    1.353842\\nH     -0.179093    0.914198   -0.521129\\nH     -1.581807    1.316033    0.494323\\nH     -1.481377    0.745653    3.011711\\nH     -0.069839   -0.119016    3.643391\\nH     -1.240716   -0.969482    2.624356\\nH     -1.553247   -0.639688   -1.001066\\n12\\n\\nO     -1.404740   -0.659061   -0.044060\\nC      0.224263    0.337484    1.427359\\nC     -0.795236    0.571294    0.320913\\nC     -0.419787   -0.169018    2.710054\\nH      0.755764    1.272432    1.635835\\nH      0.963750   -0.400686    1.095723\\nH     -0.292246    0.987712   -0.558574\\nH     -1.576732    1.269256    0.636313\\nH     -1.158985    0.546033    3.085136\\nH      0.340941   -0.308146    3.484740\\nH     -0.919524   -1.129935    2.552937\\nH     -1.549616   -0.638354   -1.005165\\n12\\n\\nO     -1.406696   -0.657427   -0.048312\\nC      0.356831    0.303040    1.286204\\nC     -0.793557    0.568680    0.324793\\nC     -0.114661   -0.308273    2.597998\\nH      0.881083    1.242198    1.494190\\nH      1.073299   -0.384025    0.821091\\nH     -0.414281    1.062174   -0.576633\\nH     -1.552312    1.214107    0.777782\\nH     -0.829679    0.350142    3.101600\\nH      0.735946   -0.462118    3.269574\\nH     -0.595039   -1.278172    2.435867\\nH     -1.549825   -0.632187   -1.009641\\n12\\n\\nO     -1.408459   -0.656323   -0.051389\\nC      0.472543    0.283917    1.129060\\nC     -0.790736    0.565839    0.327300\\nC      0.178581   -0.427901    2.441942\\nH      0.988455    1.227415    1.338321\\nH      1.149868   -0.343699    0.538340\\nH     -0.539940    1.131801   -0.576464\\nH     -1.508790    1.151111    0.909899\\nH     -0.490906    0.169277    3.069201\\nH      1.107127   -0.592511    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0.545695    0.302921\\nC     -2.226364    2.019383   -1.198021\\nH     -1.078534    2.657025    0.530016\\nH     -2.486327    1.689766    0.926111\\nH     -0.349287    0.466577    1.312971\\nH      0.059119    0.624069   -0.399173\\nH     -1.423116    2.217342   -1.914578\\nH     -2.910387    2.871618   -1.218243\\nH     -2.766854    1.136077   -1.543950\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.706092    1.755676    0.394581\\nC     -0.775609    0.545695    0.302921\\nC     -2.438841    2.055459   -0.915088\\nH     -1.114769    2.624705    0.707566\\nH     -2.435110    1.580088    1.197297\\nH     -0.230437    0.418386    1.248855\\nH     -0.032723    0.701384   -0.484310\\nH     -1.732615    2.330103   -1.704813\\nH     -3.146515    2.880049   -0.797410\\nH     -2.988867    1.175925   -1.255553\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.725724    1.710264    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-1.692926    1.582024    0.953155\\nC     -0.775609    0.545695    0.302921\\nC     -2.859203    2.004222    0.056732\\nH     -1.089040    2.455089    1.228678\\nH     -2.078082    1.170162    1.895851\\nH      0.075625    0.329908    0.964017\\nH     -0.370708    0.937332   -0.634376\\nH     -2.497391    2.507257   -0.845431\\nH     -3.533501    2.690715    0.575046\\nH     -3.433545    1.131389   -0.259998\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.641135    1.501692    1.124619\\nC     -0.775609    0.545695    0.302921\\nC     -2.917871    1.935097    0.400649\\nH     -1.036209    2.378002    1.387259\\nH     -1.899007    1.015427    2.075250\\nH      0.154192    0.321029    0.844406\\nH     -0.496874    1.012211   -0.646036\\nH     -2.681191    2.508765   -0.500871\\nH     -3.546989    2.560159    1.039482\\nH     -3.498361    1.062815    0.093764\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.566940    1.412570    1.283010\\nC     -0.775609    0.545695    0.302921\\nC     -2.933231    1.841775    0.743510\\nH     -0.962609    2.292985    1.532986\\nH     -1.695242    0.856541    2.221637\\nH      0.218281    0.322915    0.716157\\nH     -0.625792    1.082733   -0.637821\\nH     -2.822855    2.480980   -0.138013\\nH     -3.501334    2.399581    1.492316\\nH     -3.518675    0.969170    0.447131\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.471787    1.316393    1.425246\\nC     -0.775609    0.545695    0.302921\\nC     -2.904985    1.726072    1.078642\\nH     -0.869672    2.201691    1.663024\\nH     -1.470752    0.696594    2.332164\\nH      0.266642    0.335530    0.581767\\nH     -0.754952    1.147526   -0.609889\\nH     -2.919625    2.424443    0.236079\\nH     -3.397427    2.212107    1.924734\\nH     -3.494091    0.852277    0.793227\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.357527    1.215032    1.548558\\nC     -0.775609    0.545695    0.302921\\nC     -2.833683    1.590239    1.399523\\nH     -0.759208    2.105899    1.774840\\nH     -1.229908    0.538701    2.404679\\nH      0.298335    0.358628    0.443852\\nH     -0.881841    1.205329   -0.562785\\nH     -2.969619    2.340254    0.614126\\nH     -3.237288    2.001385    2.328318\\nH     -3.425089    0.714412    1.125315\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.226385    1.110460    1.650547\\nC     -0.775609    0.545695    0.302921\\nC     -2.720712    1.436921    1.699906\\nH     -0.633366    2.007472    1.866259\\nH     -0.977396    0.385935    2.437770\\nH      0.312743    0.391759    0.305095\\nH     -1.003989    1.255017   -0.497426\\nH     -2.971862    2.230051    0.988768\\nH     -3.024036    1.771518    2.695214\\nH     -3.313010    0.558256    1.436931\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -1.080912    1.004712    1.729226\\nC     -0.775609    0.545695    0.302921\\nC     -2.568271    1.269102    1.973945\\nH     -0.494596    1.908326    1.935501\\nH     -0.718132    0.241269    2.430794\\nH      0.309586    0.434279    0.168199\\nH     -1.119018    1.295622   -0.415083\\nH     -2.926312    2.095980    1.352713\\nH     -2.761820    1.526979    3.018280\\nH     -3.160037    0.386851    1.722010\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -0.923940    0.899848    1.783065\\nC     -0.775609    0.545695    0.302921\\nC     -2.379328    1.090048    2.216306\\nH     -0.345599    1.810392    1.981219\\nH     -0.457163    0.107519    2.383886\\nH      0.288924    0.485360    0.035826\\nH     -1.224689    1.326354   -0.317360\\nH     -2.833854    1.940650    1.698878\\nH     -2.455745    1.272529    3.291229\\nH     -2.969146    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-0.922271    0.255515    3.792250\\nH     -1.910186   -0.575524    2.578914\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC     -0.077958    0.457416    1.660765\\nC     -0.775609    0.545695    0.302921\\nC     -1.036309    0.152019    2.814162\\nH      0.447555    1.402737    1.842531\\nH      0.699816   -0.316502    1.608125\\nH     -0.051560    0.829709   -0.473803\\nH     -1.549183    1.318254    0.327126\\nH     -1.760466    0.961938    2.946360\\nH     -0.498991    0.026252    3.757598\\nH     -1.597411   -0.762740    2.613154\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.080320    0.397411    1.561352\\nC     -0.775609    0.545695    0.302921\\nC     -0.724785   -0.018478    2.794587\\nH      0.594122    1.348762    1.745132\\nH      0.869987   -0.340760    1.365952\\nH     -0.157778    0.906948   -0.531341\\nH     -1.564433    1.284503    0.469565\\nH     -1.452933    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-1.447441    1.062222    0.994074\\nH     -0.104940    0.002795    3.020720\\nH      1.453341   -0.756102    2.673203\\nH     -0.045320   -1.477516    2.062987\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.652243    0.302302    0.793076\\nC     -0.775609    0.545695    0.302921\\nC      0.722925   -0.607810    2.021379\\nH      1.113956    1.272889    1.011289\\nH      1.237452   -0.134052   -0.027763\\nH     -0.768829    1.254528   -0.537151\\nH     -1.376547    0.990251    1.101102\\nH      0.228662   -0.144429    2.880931\\nH      1.757584   -0.818378    2.303763\\nH      0.220281   -1.556656    1.823731\\nH     -1.578000   -0.649350   -1.009564\\n12\\n\\nO     -1.456032   -0.656478   -0.054778\\nC      0.707088    0.326865    0.606197\\nC     -0.775609    0.545695    0.302921\\nC      0.950881   -0.656924    1.752822\\nH      1.161102    1.298591    0.835226\\nH      1.204215   -0.032766   -0.304947\\nH     -0.890315    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-       "\tviewergrid_1711055601674338[0][0].setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewergrid_1711055601674338[0][0].animate({\"loop\": \"forward\", \"reps\": 0, \"step\": 1, \"interval\": 120});\n",
-       "\tviewergrid_1711055601674338[0][0].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n",
-       "\tviewergrid_1711055601674338[0][1].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n",
-       "\tviewergrid_1711055601674338[0][0].zoomTo();\n",
-       "\tviewergrid_1711055601674338[0][1].addModelsAsFrames(\"12\\n\\nO     -1.430607   -0.643928   -0.065245\\nC      0.614629    0.632252   -0.310563\\nC     -0.763861    0.546265    0.331805\\nC      1.528625   -0.503959    0.125286\\nH      1.078230    1.589740   -0.048792\\nH      0.515190    0.597806   -1.401663\\nH     -1.380179    1.401181    0.036810\\nH     -0.685807    0.545485    1.423549\\nH      1.667038   -0.500240    1.210977\\nH      2.512567   -0.397385   -0.342589\\nH      1.121131   -1.476953   -0.166516\\nH     -1.565503   -0.595594   -1.026984\\n12\\n\\nO     -1.433551   -0.644110   -0.065775\\nC      0.530800    0.727199   -0.457217\\nC     -0.760833    0.543563    0.329679\\nC      1.522704   -0.400812   -0.213531\\nH      0.992580    1.681420   -0.180334\\nH      0.306047    0.765792   -1.529578\\nH     -1.433967    1.389638    0.160922\\nH     -0.559140    0.480576    1.403609\\nH      1.798447   -0.456556    0.844340\\nH      2.435640   -0.232263   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-0.608411\\nH     -1.177052    2.579071    0.909721\\nH     -2.384767    1.385517    1.411532\\nH     -0.112211    0.384882    1.142212\\nH     -0.099012    0.824193   -0.572690\\nH     -1.988407    2.366340   -1.459200\\nH     -3.271183    2.879782   -0.350184\\nH     -3.220235    1.206657   -0.922697\\nH     -1.631824   -0.640713   -0.970152\\n12\\n\\nO     -1.444965   -0.632805   -0.014424\\nC     -1.724860    1.633163    0.773858\\nC     -0.753096    0.574531    0.271340\\nC     -2.741538    2.034889   -0.284994\\nH     -1.164404    2.519795    1.089702\\nH     -2.266067    1.251150    1.647214\\nH     -0.013107    0.355339    1.047369\\nH     -0.215778    0.907187   -0.623350\\nH     -2.243234    2.427809   -1.177027\\nH     -3.404224    2.814080    0.104841\\nH     -3.362425    1.184613   -0.584107\\nH     -1.631971   -0.641916   -0.968782\\n12\\n\\nO     -1.442678   -0.634182   -0.011402\\nC     -1.695033    1.563213    0.954932\\nC     -0.757344    0.577671    0.271714\\nC     -2.841880    1.991176    0.050532\\nH     -1.129175    2.449295    1.263223\\nH     -2.116811    1.105249    1.857068\\nH      0.078034    0.338839    0.937085\\nH     -0.344187    0.989390   -0.655450\\nH     -2.464823    2.459127   -0.864423\\nH     -3.477970    2.717661    0.566017\\nH     -3.466400    1.137710   -0.231677\\nH     -1.630145   -0.647183   -0.965367\\n12\\n\\nO     -1.444124   -0.634243   -0.011135\\nC     -1.640545    1.485180    1.124911\\nC     -0.761784    0.578722    0.274565\\nC     -2.900743    1.921441    0.391720\\nH     -1.069520    2.372073    1.420712\\nH     -1.936164    0.957703    2.039140\\nH      0.152362    0.325945    0.820251\\nH     -0.473155    1.065670   -0.663140\\nH     -2.651384    2.468212   -0.523237\\nH     -3.498516    2.580942    1.028733\\nH     -3.522071    1.062227    0.120448\\nH     -1.627441   -0.648808   -0.965869\\n12\\n\\nO     -1.438666   -0.637096   -0.009566\\nC     -1.566120    1.394855    1.283314\\nC     -0.765214    0.580849    0.276547\\nC     -2.916435    1.829381    0.731534\\nH     -0.992248    2.282107    1.572564\\nH     -1.736513    0.797718    2.186628\\nH      0.216878    0.331858    0.689972\\nH     -0.612300    1.140781   -0.652625\\nH     -2.793136    2.438412   -0.169652\\nH     -3.452807    2.427813    1.474690\\nH     -3.540827    0.966178    0.480481\\nH     -1.620650   -0.653738   -0.964394\\n12\\n\\nO     -1.435256   -0.639258   -0.009262\\nC     -1.470076    1.298840    1.424648\\nC     -0.768668    0.581701    0.279425\\nC     -2.889172    1.714644    1.064167\\nH     -0.893822    2.187652    1.704498\\nH     -1.513495    0.639623    2.299363\\nH      0.263641    0.345604    0.554759\\nH     -0.749344    1.207120   -0.619943\\nH     -2.892504    2.383702    0.197690\\nH     -3.351832    2.243828    1.903124\\nH     -3.511815    0.845415    0.829976\\nH     -1.613947   -0.656370   -0.964874\\n12\\n\\nO     -1.432096   -0.641615   -0.009585\\nC     -1.355046    1.197889    1.546874\\nC     -0.771725    0.581850    0.282585\\nC     -2.819209    1.580151    1.382405\\nH     -0.777841    2.088828    1.817072\\nH     -1.274154    0.483666    2.374497\\nH      0.293002    0.369072    0.417362\\nH     -0.882401    1.264530   -0.567068\\nH     -2.945800    2.302781    0.569849\\nH     -3.195750    2.036203    2.303605\\nH     -3.437775    0.704218    1.163328\\nH     -1.606671   -0.657826   -0.965923\\n12\\n\\nO     -1.427595   -0.644006   -0.010740\\nC     -1.223551    1.093551    1.647725\\nC     -0.774753    0.582514    0.285390\\nC     -2.708094    1.427963    1.680933\\nH     -0.646581    1.987553    1.908707\\nH     -1.022610    0.331844    2.410000\\nH      0.304710    0.404492    0.279093\\nH     -1.010622    1.313006   -0.496190\\nH     -2.953280    2.193696    0.938082\\nH     -2.986283    1.811353    2.667749\\nH     -3.320536    0.543692    1.478616\\nH     -1.598457   -0.658403   -0.967722\\n12\\n\\nO     -1.424256   -0.646597   -0.012040\\nC     -1.077814    0.988379    1.725028\\nC     -0.777927    0.582119    0.288510\\nC     -2.557557    1.262321    1.953218\\nH     -0.501882    1.885554    1.976853\\nH     -0.764533    0.188156    2.405563\\nH      0.298197    0.446685    0.144844\\nH     -1.128998    1.350221   -0.408987\\nH     -2.912105    2.064997    1.298752\\nH     -2.728765    1.569312    2.989847\\nH     -3.162321    0.370355    1.762453\\nH     -1.591193   -0.658514   -0.969878\\n12\\n\\nO     -1.420523   -0.648145   -0.015409\\nC     -0.920794    0.883695    1.777545\\nC     -0.781407    0.582786    0.291369\\nC     -2.370788    1.084027    2.194981\\nH     -0.347603    1.784945    2.021095\\nH     -0.503606    0.054551    2.360761\\nH      0.274197    0.499055    0.015799\\nH     -1.237361    1.378751   -0.307034\\nH     -2.827628    1.908947    1.638985\\nH     -2.426552    1.322305    3.261898\\nH     -2.964150    0.181049    2.020434\\nH     -1.583966   -0.655868   -0.973792\\n12\\n\\nO     -1.416102   -0.651419   -0.017744\\nC     -0.755624    0.782255    1.803872\\nC     -0.785309    0.582370    0.294619\\nC     -2.151022    0.899795    2.400434\\nH     -0.185315    1.687433    2.039531\\nH     -0.246936   -0.065933    2.276582\\nH      0.233049    0.558214   -0.105210\\nH     -1.334775    1.394386   -0.193050\\nH     -2.694294    1.742819    1.961708\\nH     -2.086698    1.063180    3.480907\\nH     -2.734532   -0.010729    2.232044\\nH     -1.576903   -0.655798   -0.976687\\n12\\n\\nO     -1.412681   -0.653351   -0.021720\\nC     -0.585579    0.685648    1.803986\\nC     -0.788914    0.581888    0.298469\\nC     -1.902984    0.710434    2.566042\\nH     -0.019786    1.595466    2.032532\\nH      0.001940   -0.170628    2.155174\\nH      0.176641    0.622745   -0.215227\\nH     -1.416962    1.400301   -0.068181\\nH     -2.521088    1.557328    2.251231\\nH     -1.715564    0.808139    3.640069\\nH     -2.473278   -0.209961    2.406388\\nH     -1.570519   -0.653167   -0.981201\\n12\\n\\nO     -1.409142   -0.655458   -0.026049\\nC     -0.413899    0.596104    1.777717\\nC     -0.792443    0.581033    0.302815\\nC     -1.631236    0.520672    2.688510\\nH      0.146384    1.510967    1.999813\\nH      0.237866   -0.257282    1.998549\\nH      0.105407    0.692495   -0.313429\\nH     -1.484991    1.394840    0.065334\\nH     -2.308155    1.361603    2.506867\\nH     -1.319957    0.556071    3.737306\\nH     -2.187773   -0.408944    2.533711\\nH     -1.564484   -0.650724   -0.985891\\n12\\n\\nO     -1.407998   -0.656259   -0.030722\\nC     -0.243714    0.514166    1.725627\\nC     -0.794763    0.579495    0.307606\\nC     -1.341048    0.334602    2.764982\\nH      0.311286    1.433431    1.942624\\nH      0.454972   -0.326289    1.810221\\nH      0.021961    0.763965   -0.397810\\nH     -1.534387    1.379725    0.205214\\nH     -2.060889    1.158027    2.720033\\nH     -0.908778    0.316472    3.770505\\nH     -1.882591   -0.604516    2.614631\\nH     -1.560726   -0.646529   -0.990964\\n12\\n\\nO     -1.405111   -0.658692   -0.034736\\nC     -0.078902    0.444557    1.648641\\nC     -0.796758    0.577194    0.312372\\nC     -1.038140    0.154947    2.794355\\nH      0.467574    1.369817    1.861402\\nH      0.652060   -0.370506    1.593829\\nH     -0.074126    0.838644   -0.467830\\nH     -1.568913    1.351904    0.348267\\nH     -1.779160    0.954400    2.895224\\nH     -0.487600    0.081669    3.737641\\nH     -1.569570   -0.789422    2.640660\\nH     -1.555501   -0.645562   -0.995230\\n12\\n\\nO     -1.405377   -0.658040   -0.040185\\nC      0.078537    0.383347    1.548528\\nC     -0.796594    0.574715    0.317245\\nC     -0.729135   -0.013841    2.775937\\nH      0.619263    1.312801    1.758334\\nH      0.821214   -0.399079    1.353842\\nH     -0.179093    0.914198   -0.521129\\nH     -1.581807    1.316033    0.494323\\nH     -1.481377    0.745653    3.011711\\nH     -0.069839   -0.119016    3.643391\\nH     -1.240716   -0.969482    2.624356\\nH     -1.553247   -0.639688   -1.001066\\n12\\n\\nO     -1.404740   -0.659061   -0.044060\\nC      0.224263    0.337484    1.427359\\nC     -0.795236    0.571294    0.320913\\nC     -0.419787   -0.169018    2.710054\\nH      0.755764    1.272432    1.635835\\nH      0.963750   -0.400686    1.095723\\nH     -0.292246    0.987712   -0.558574\\nH     -1.576732    1.269256    0.636313\\nH     -1.158985    0.546033    3.085136\\nH      0.340941   -0.308146    3.484740\\nH     -0.919524   -1.129935    2.552937\\nH     -1.549616   -0.638354   -1.005165\\n12\\n\\nO     -1.406696   -0.657427   -0.048312\\nC      0.356831    0.303040    1.286204\\nC     -0.793557    0.568680    0.324793\\nC     -0.114661   -0.308273    2.597998\\nH      0.881083    1.242198    1.494190\\nH      1.073299   -0.384025    0.821091\\nH     -0.414281    1.062174   -0.576633\\nH     -1.552312    1.214107    0.777782\\nH     -0.829679    0.350142    3.101600\\nH      0.735946   -0.462118    3.269574\\nH     -0.595039   -1.278172    2.435867\\nH     -1.549825   -0.632187   -1.009641\\n12\\n\\nO     -1.408459   -0.656323   -0.051389\\nC      0.472543    0.283917    1.129060\\nC     -0.790736    0.565839    0.327300\\nC      0.178581   -0.427901    2.441942\\nH      0.988455    1.227415    1.338321\\nH      1.149868   -0.343699    0.538340\\nH     -0.539940    1.131801   -0.576464\\nH     -1.508790    1.151111    0.909899\\nH     -0.490906    0.169277    3.069201\\nH      1.107127   -0.592511    2.997765\\nH     -0.288423   -1.402832    2.270984\\nH     -1.549316   -0.628247   -1.012897\\n12\\n\\nO     -1.409683   -0.655575   -0.053966\\nC      0.569311    0.280110    0.958978\\nC     -0.787257    0.563005    0.328370\\nC      0.454169   -0.525659    2.245202\\nH      1.076199    1.227748    1.171542\\nH      1.192172   -0.281172    0.252934\\nH     -0.666989    1.195355   -0.557865\\nH     -1.447687    1.081716    1.030061\\nH     -0.153035    0.004469    2.985878\\nH      1.446628   -0.691569    2.676402\\nH     -0.001260   -1.504103    2.063497\\nH     -1.547791   -0.625677   -1.015911\\n12\\n\\nO     -1.412221   -0.653831   -0.056824\\nC      0.645529    0.289780    0.778210\\nC     -0.783842    0.560475    0.329481\\nC      0.708004   -0.599714    2.011751\\nH      1.145211    1.240614    0.993620\\nH      1.197096   -0.201566   -0.031761\\nH     -0.793869    1.253533   -0.518609\\nH     -1.369708    1.006754    1.139114\\nH      0.173604   -0.143101    2.850945\\nH      1.748859   -0.751527    2.314656\\nH      0.267645   -1.582601    1.817228\\nH     -1.548223   -0.620302   -1.018875\\n12\\n\\nO     -1.413660   -0.653164   -0.058607\\nC      0.699182    0.315717    0.591983\\nC     -0.780875    0.558106    0.330004\\nC      0.934592   -0.649294    1.745284\\nH      1.189883    1.269787    0.813999\\nH      1.167173   -0.099201   -0.308282\\nH     -0.916683    1.302018   -0.462284\\nH     -1.279228    0.929141    1.231214\\nH      0.493139   -0.267221    2.671258\\nH      2.008194   -0.783368    1.911367\\nH      0.501101   -1.632577    1.537334\\nH     -1.547282   -0.617248   -1.021032\\n12\\n\\nO     -1.416347   -0.650904   -0.060717\\nC      0.729874    0.354300    0.402299\\nC     -0.777341    0.556206    0.330024\\nC      1.128924   -0.672764    1.452454\\nH      1.214169    1.310116    0.629892\\nH      1.100876    0.015547   -0.572053\\nH     -1.031160    1.342088   -0.389090\\nH     -1.175197    0.850893    1.306221\\nH      0.787400   -0.367925    2.447031\\nH      2.218273   -0.775339    1.484554\\nH      0.704794   -1.656688    1.229380\\nH     -1.548104   -0.611885   -1.023307\\n12\\n\\nO     -1.419774   -0.648892   -0.062766\\nC      0.736315    0.406214    0.212962\\nC     -0.774345    0.553645    0.331310\\nC      1.288953   -0.669491    1.137279\\nH      1.214524    1.363678    0.446748\\nH      0.999024    0.144641   -0.818666\\nH     -1.137460    1.372864   -0.297967\\nH     -1.061296    0.770040    1.365240\\nH      1.056465   -0.443798    2.182881\\nH      2.377370   -0.729624    1.037443\\nH      0.874244   -1.653446    0.897025\\nH     -1.551209   -0.604876   -1.025227\\n12\\n\\nO     -1.421059   -0.649057   -0.064461\\nC      0.718856    0.472469    0.031015\\nC     -0.771870    0.550959    0.331288\\nC      1.409967   -0.640007    0.806367\\nH      1.190214    1.430707    0.276245\\nH      0.867424    0.292298   -1.039945\\nH     -1.232988    1.390835   -0.198249\\nH     -0.943332    0.692755    1.402835\\nH      1.303447   -0.488964    1.885230\\nH      2.478567   -0.656729    0.569866\\nH      0.994183   -1.620332    0.553229\\nH     -1.550888   -0.603566   -1.026835\\n12\\n\\nO     -1.423592   -0.647776   -0.065681\\nC      0.678341    0.549113   -0.143446\\nC     -0.768559    0.548603    0.331374\\nC      1.489737   -0.585057    0.466273\\nH      1.145191    1.506172    0.112546\\nH      0.705387    0.446307   -1.234557\\nH     -1.314950    1.400098   -0.087102\\nH     -0.818826    0.618505    1.422603\\nH      1.511981   -0.506269    1.558049\\nH      2.521492   -0.548278    0.102426\\nH      1.074047   -1.561770    0.200109\\nH     -1.553524   -0.600805   -1.028348\\n\",\"xyz\");\n",
-       "\tviewergrid_1711055601674338[0][1].setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
-       "\tviewergrid_1711055601674338[0][1].animate({\"loop\": \"forward\", \"reps\": 0, \"step\": 1, \"interval\": 120});\n",
-       "\tviewergrid_1711055601674338[0][0].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n",
-       "\tviewergrid_1711055601674338[0][1].setHoverable({},true,\"function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.atom+\\\":\\\"+atom.index,\\n                {\\n                    position: atom,\\n                    backgroundColor: 'black',\\n                    fontColor: 'white',\\n                    alignment: 'center',\\n                    showBackground: true,\\n                    backgroundOpacity: 0.2\\n                }\\n            );\\n        }}\",\"function(atom,viewer) {\\n            if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n            }\\n        }\");\n",
-       "\tviewergrid_1711055601674338[0][1].zoomTo();\n",
-       "viewergrid_1711055601674338[0][1].render();\n",
-       "viewergrid_1711055601674338[0][0].render();\n",
-       "});\n",
-       "</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<py3Dmol.view at 0x177271650>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "viewer = grid_viewer(viewer_grid=(1, 2), viewer_size=(800, 400), linked=True)\n",
     "\n",
diff --git a/ipython/Handle molecule with XYZ.ipynb b/ipython/Handle molecule with XYZ.ipynb
index f198a47d..be37db3c 100644
--- a/ipython/Handle molecule with XYZ.ipynb	
+++ b/ipython/Handle molecule with XYZ.ipynb	
@@ -7,17 +7,17 @@
     "# A Demo of using RDMC with XYZs\n",
     "Author: Xiaorui Dong\n",
     "\n",
-    "This notebook demonstrates how RDMC handles XYZ. Molecules can be perceived by using [openbabel](http://openbabel.org/wiki/Main_Page) / [method from Jensen et al.](https://github.com/jensengroup/xyz2mol). The created molecule can be visualized by `mol_viewer` and exported to xyz format."
+    "This notebook demonstrates how RDMC handles XYZ. Molecules can be perceived by using [openbabel](http://openbabel.org/wiki/Main_Page) / [xyz2mol](https://github.com/jensengroup/xyz2mol). The created molecule can be visualized by `mol_viewer` and exported to xyz format."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from rdmc.mol import RDKitMol\n",
-    "from rdmc.view import mol_viewer\n",
+    "from rdmc.mol import Mol\n",
+    "from rdtools.view import mol_viewer\n",
     "from rdkit.Chem.Draw import IPythonConsole"
    ]
   },
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,31 +72,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Generate an RDKitMol from xyz\n",
+    "## 2. Generate an Mol from xyz\n",
     "**Arguments:**\n",
-    "- `backend`: choose the `openbabel` or `jensen` backend\n",
+    "- `backend`: choose the `openbabel` or `xyz2mol` backend\n",
     "- `header`: indicate if the str includes lines of atom number and title"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<rdmc.mol.RDKitMol at 0x14f099f60>"
+       "<rdmc.mol.rdkitmol.RDKitMol at 0x7f3a0f8684f0>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "RDKitMol.FromXYZ(xyz, backend='openbabel', header=True)"
+    "Mol.FromXYZ(xyz, backend='openbabel', header=True)"
    ]
   },
   {
@@ -108,63 +108,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
-       "<rdmc.mol.RDKitMol at 0x14f09a230>"
+       "<rdmc.mol.rdkitmol.RDKitMol at 0x7f3a0e78a1b0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "RDKitMol.FromXYZ(xyz_without_header, backend='openbabel', header=False)"
+    "Mol.FromXYZ(xyz_without_header, backend='openbabel', header=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use `jensen` backend. For most cases, Jensen's method returns the same molecule as using `openbabel` backend"
+    "Use `xyz2mol` backend. For most cases, xyz2mol's method returns the same molecule as using `openbabel` backend"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<rdmc.mol.RDKitMol at 0x14f099750>"
+       "<rdmc.mol.rdkitmol.RDKitMol at 0x7f3a0e789e90>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "RDKitMol.FromXYZ(xyz, backend='jensen', header=True)"
+    "Mol.FromXYZ(xyz, backend='xyz2mol', header=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The optional arguments for the Jensen et al. method are provided."
+    "The optional arguments for the xyz2mol method are provided. Note, RDKit starts to have a native xyz2mol since version 2022.09, but you can choose to use the original python version (with minor modifications to work with RDMC) with `force_rdmc = True`. The reason to support both is that (1) we introduce the python version to rdmc since 2021 and (2) RDKit's native is not always reliable (e.g., rdkit-2023.09.6-macosarm64 version run into erros)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -176,26 +176,27 @@
     },
     {
      "data": {
-      "image/png": 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6aRilaoqG0YaUkEDU1QlAevcm6ekV9xYWkh9/5Me+K1dEVuLtTQwNya+/ksBAUlhI2GwhYTQ/nxgbE4B4e5fbzuORvn0JQNTV+R1S6Td27FgnJ6e9e/e+e/dOsPHkyZNM7Hv06FG1NcTHxx8+fPjw4cMZGRmV99IwStUUDaMNackS/p17XJzwAvn5pGtXAhArK5GVFBeX+1ZoGGWq8vcnlX/zjx3jd0ifPatp86UFl8tdtGgRgBYtWuTl5VVb/vLly0ygfPHiReW9NIxSNdV039JKPUJw9CgA/PAD2rcXXkZODsw65mFhePWK2Zabm5uTk1NaRsw36XJyGD1ayLLvgjct5UfzSDtCyNOnTzdv3jx48GA9PT1mXpOqqqq8vLykm0Y1OzSMirR7924DA4MqprW8fPnSwMDAwMAgOjpayO6YGKSkAMCwYQUFBb6+vj169DAyMtq+fXu5YsOHM/99cuqUi4uLro6OkpKSsrKyrqbmtGnThNdcI0wbAGhq1rWqpmTlypUTJkxISEhYtGhRUlISs7CHnHgDYIcMGZKcnJycnPzNN980cDOpZoEOiBOJw+F8+PChigL5+flMgQKhq7C9eQMgEzgSHr5l4UJB3iBmAGMpHR1oanp//rz4t9+KCQHQCsgGPqWn+/r6nj171t/ff+jQobW/jHv3AKBVK9QhBWcTtH79+o0bNwq+VVNTA/DlyxdxjpWTk9PR0WmollHND+2NNpiMjGtAG2CBl1dCQkLPnj3btm0rtKC/nNwCQIbF2rZtGycqKt3AINfA4Nratfr6+rm5uVOmTMnNza1lGzIzcfAgAEyahK9rDLlgSDyXy7106ZKmpiaAnJycpKQkibaLao5oGG1A3YG+wK7ffouLiwsPDxfkA67gY1FRS+DAgAELFy5UMzFBbCw7Ntbew2Pv3r0AUlJS7ty5I/IcJXMZhZs9G1++QF0dy5fX6UqaqkuXLrVp02bnzp2C5ZHv3r0r2SZRzdBX1UNpWlq10gX+BeDkJPIVEwBgLiEugIaVVYXtZmZmzAdmQmRZ5PVr1oYNCArCkydITETJkpPlbN2K06fBYmHfPrRpU+vraMoGDx6cnJyspKSUn5/funXr1NRUHx+f8ePHS7pdVPNCe6MNRpB3ruQVvHApKUhL0wBgbFxhT2RkJPPBxMQEwNOnT728vAghAAr37sWaNQgKwpcvePZMSLU7dmDpUgDYsgUTJtTlOpoyJSUlJSUlAPLy8rNmzQJw69atv/76S9LtopoXGkbr08yZM3v37r2UiV8mJvyX4zdvVnXMjRv8DzY2ZTd//vyZyVHUt29fS0tLADt37ly+fDkTRj906IB583D+PFJT8e235Srk8bByJRYtAiHYtAmLFtXTxTV1K1asMDU1BTBnzpzFixeXfUjK4XD+/PPPYcOGFRcXA7h586a+vr6+vn5UVJTEmkt9RehNfTW+fPmyefNmobsSExMrbImMjAwLC+MvX85iwcUFu3fj1CmsW4fyC1jy8XjYuRMAevRA167Z2dknTpzIysqKioo6ffp0VlZWnz59Lly4wJR1cnKSl5dn+fiAkE4LF2LcOCEVcrmYMgX+/pCTw+7dkl+ZrhEpKioGBgY6ODg8f/5827Zt27dvNzIy0tDQSExMFAyTuHHjhr29fV5eHvP/rrCwkNn+9u1bZshUWYL18ry9vX/99dfGug5K+tAwWg0Oh+Pu7i5m4YEDB+rp6fXu3Zv//YIFOHQIHA6mTUPJ3JhyPDwQGgoAK1cy52LuTBlmZmaHDh0SLCA8atSoUaNG4a+/ICop16tXmDABL15ATw9//12he9sctGnTJiwszMfHx9fX98mTJ2/evGG2y8vL29jYTJw4ceDAgQA6duzIpMvT/LrG0lKSQsNoNdTU1CpkqBRITk4+cOBA2S2enp7lShgZYft2zJqFq1dhY1M6xai4GKGh2LIFTE9z6lSma6muru7j5ZW3bNlH4K6xcUhEhKmp6Z49e2YPGICy85oAvHuH8HAAUFGBiQkA7N+P+fORmws9Pfj4QE6OX0BAWxvt2tX2xyA15OTk5s2bN2/evKysrMTERC6Xq6Wlpa+vXzZnqKmp6U7mJqCEkZERkdqMkb/99ltcXJydnd0EEQ/BL168ePnyZV1d3Q0bNlRRDyHk2rVr/v7+0dHR2dnZrVu3trS0/PHHH42a9uoyTYJEp6I2acy/ufbt24sqEF4Sp4ROzS514ABRViaAAwDATU6uNJ0dm01+/ZUUFZUWjo/n7zp+/PfffwfAYrGedu4sPM8eQL77jhBCnj4VWUDwNX9+ff1kJOXt27c7duyYNGnSsGHDHBwcZsyYcfLkSS6XW7ZMfn5+REREUVERISQrKysuLo4QkpiY6OXlNWXKlCFDhjg6Oq5du/bNmzeSuYYGwGRQnS/6/+/KlSsBGBsbV1HJly9f7O3tK8cHOTm5PXv2NECrvyq0N9rwXF3h4IA9e7B7N758QUEB5OXRrh3s7DBrFrp3L1dYUxPHjwNA377/a99+69ataWlpBwjZ1auX8MqZrqi8PEQVEBAx+F8qZGdnz58//8iRI8w7IoFDhw61bdt2//79w4YNY7YUFhbevXv34sWLS5cuVVVVlZGRWb16taenZ9mZZgEBAZ6enn/88ceCBQsa9TKasLlz516/fl1GRmbVqlUuLi6tWrWKiYlZs2bNrVu3fvnlFxMTEzs7O0m3sQmTdBxvuuqtN1rCwcEBgNvMmeK3wdbWFoCDg4P4h3x9srOzra2tmR919+7dt2zZEhAQEBAQsHHjRkNDQwBycnJ37typfGDZpUecnZ3Pnj17+/btAwcOCN4m+fv7N/7l1Lu690Y5HA6zeN/ChQvLbs/Ozm7Xrh2AcePG1WeLvzq0N9rohGVs4nA4FZYbYnz69AmAijQs29lw/ve//4WGhgJYtWrVunXrBC/QR40atWDBgunTpysqKpa+1iujVatWHA4HwN69e2fPns1s7N+///fff9+5c+dPnz55enqOHj26sa6j6YqPj2cGLfTv37/sdiUlJWtr6/j4+Ldv30qoadKBjhutT8zt5MOHD2t01Pnz5w0NDW8IBpCWuHTpUkxMDABmBeDmKTExkRlOP3bs2A0bNrDL/xFSVFQ8ceLE4cOHhebHY/KPuLu7C2IoQ01N7aeffgIQHh5OV/0E0KpVK+aDYGyDADODjj+GjxKB9kbrk7u7e0hIiKOjo2BZ89DQ0Gcls4zi4+MBvHr1SjDNxtXVVUZG5tKlSxkZGfb29q6urmPGjGnbtm1aWlpgYKC3tzchxNDQcNq0aZK4mibh1KlTTEdpzZo1QgvIyMiIOlZLSwuA0FSHzMQwHo/3+fPnli1b1k9bpURWVtamTZsAODs7W1hYAGjTpo2pqWlkZKSnp2evXr2YYWEAIiIimHwOzBp/lCg0jDas8+fPVxi9f/fuXUH6jKlTp8rIyBw4cEBXV3fLli379+/fv39/2cIWFhZ+fn7N+aY+JCQEgIGBAfMLX7WbN2+GhoYqKyvPnz8fJWE0LS2tcknBkvRCn6VIo7S0tGdCpwUDKYKcswCArKwsZmRe586dmZ8qi8XatWuXg4MDh8MZPHiwnZ3dtGnTTE1Nx40bV1xcvHTpUmdn50a4BOlFw6hIVlZWv/zyi4aGhqgC2trav/zyC8qM4j59+nROTk7ZqGdtbe3m5iaqBqYnJSMj8/vvv0+fPv3MmTMPHz5MSUlRUFAwMjJycHAYM2YM8+y/2WLma5qbm4tT+MqVK9u3b9fW1mbCKPP/RWgYZR68GBsbM4lKvwInTpw4ceKEOCXZbDbTAS/7T2vQoEHXr18fOnRoYWFhYGBgYGAgs33jxo3Lv9L0YPWIhlGR7O3thY6kE2jbtu2uXbvKbmlfKZPT2LFjx44dK87pTExMVq9eXdNGfvWYbiPTr6wWm81ms9mC23wmjFbOj5WUlOTn5wfAxcWlPtsqUTo6OpX/+TE+fvxYduKyrq5uVlZWhTIRERGzZ88uLCy0s7Pr2LHj33//nZ6eDmD16tVJSUnbtm1r8XXlq61nkh4qQFFVYV5uzKzJKDEB5hl0hWN5PJ6joyOA1q1bf/78uZ6aKUl1H/CUnJzM/JynTp1aXFxMCMnLy/v7778HDBjARInJkyc3SNO/FvRNPdWkMTfdmZmZtThW6LNRLy+vgIAANpu9f//+Kp7YNCvbtm1LSkrS1NT8888/mbEQ8vLyY8eODQ4OZmZCHz9+/PHjx5JuZtNFwyjVpDErr9Quo13lMLp9+/YVK1awWKw9e/bQEaMC//33H4C+ffsyyVvL8vDwYD48evSokVslRWgYpZo0Zlx9ZGRkhdfN4igbRgkha9asYZaz37p1a4WRpM0cs9iXIG1gWaySfDrC122kANAwWj9iYnDhAg4cwNGj+PdfiLc+ZUWRkQgLg5ijwTMyEB7O//r0qTankxJOTk4AioqKfHx8anqsIIxmZ2c7Oztv2LBBTk5u7969CxcurP+GSpXc3Nzc3FxBggIm3XVISMinSv+WLl++zHwQZ8BZ8yXph7PSjMcjvr7ExKRiLqUWLYiTE3n5Utx67t4lI0fy0z79/LNYhzg6lp5uy5ZaX4FUYBILKCoq/vfffzU6sKioaNy4cRMnTmRWtdLR0bl7924DNbKR8Xg8Ly+v169fk5q/Yvr48SPzu3/06FFmS0hICPNI9Ntvvy2bIOLChQvMBCdLS0sej9eQFyTdaBitrYICMnEiP5CpqRFHR/LLL2TGDNK7N2GzCUAUFUnVmS/y84mvL+nWrVwIFieMHj/OP6mGRnMIo9HR0cxQXCUlpQ0bNiQkJDDbs7Ozg4ODJ0+e7Orqymxh7tl1dHQEx/r6+iorKwMwNTV9/vx5eiW5ubkSuKS64XK5zHh4U1PTwsLCuodRQsiuXbuYIU1sNtvAwMDa2lpbW5spZmxsHB8f3+BXJc1oGK2txYv5gW/hQpKVVW7X48fE1JQARF6ePHsmsoYzZwhAZGRI375k82bSq5dYYTQtjWhrE4Ds3k0MDZtDGCWEPHr0qE2ZxU2ZTMyCb1u2bMlEQ+ZuXV5enul1Xrt2rdq7sW3btonTgGPHjjmXCA4ObtirrVJ8fDyzNpeKisrFixcJIWPGjDE2Nvb09BR1yPbt242NjYcOHcp8y+Vyd+zYsWPHjlevXpUt9uTJkylTpgh+zjIyMt27d/f09MzJyWnQK/oK0DBaKy9e8LucCxYIL/DpE9HTIwDp21dkJXl55OBB8ukT/9shQ8QKo5MmEYBYWZGiouYTRgkhXC5306ZN1tbWgnHgsrKyZmZmixYtelny/ESQ3uX48eOEkOvXrxtWoqioyERe5tuDBw8SQv7555+YmBhRp37//j0z7Yd533LkyJHGueTK7t27x+Rb6dSp00vxnxrVUHZ2dnp6OjOAlBIHDaO1MmcOAUirViQ7W2SZAwf43dXHj8WqU5wwevUq/9krU2dzCqNlpaampqWlFZVdNYAQQojgnQkTRoViZqa5uLgItnC5XB0dHTab7ezsLDQr/siRIwE4OTkxzxYkFUZ9fHzk5OQA2NvbZ2RkSKQNlFD0TX2t/PsvADg5odI4u1I//AA5udLCAD58wLt3qDQ3UVzZ2Zg7FwAWLECPHrWsRDoVFBTMmDHjwYMHzLdaWlqamppV5Haqkby8PEdHRxkZmXPnzpmams6bN6/s4szHjh27fPmyiopKheWbGlNRUdGyZctmzZpVUFDg5uZ2+fLlryajyteBhtGa43DAZLG1sqqqmLIyTE0BlC4t16cPjIywdm0tz7t8Od6/R/v2ta9BOnG53FGjRh0+fHjSpElCxzbWkaam5l9//RUTE+Pm5sbj8Xbv3t2xY8dZs2YlJyenpaUtXrwYwIYNG9pKaBWWz58/29vbb9q0SV5e/vDhwz4+PnR6e1NDw2j1CCGurq4jRoxgUqlDMA68zEsP4ZjXIKmp9dCI0FD8+ScA7NqF5pQfMz09fejQof/++6+Ojo6fn1/D5djZdXkAABRaSURBVLsyMDDw8fF58eLFlClTCgsL//rrr06dOg0YMCA1NdXCwmIucx/Q6J4/f25lZXXr1q02bdrcuXOnOWeebcpoGK1ecnLyoUOHrl69yuS+RHY2f4eiYjVHKisDgCCbjo8Pzp6Fq2uNW1BQAFdXFBdj3Dg4Otb4cKkVFxfXp0+fkJCQjh073r17t0cDPMqIiopiFmROSEgA0KVLl6NHjz579szZ2TknJycyMhKAubl5ToUFrhvF5cuXbW1t379/37NnzwcPHghdKIVqCujdQfX09PR27tyZmJg4ePBgABCkE632V4uZkiTIaDliRC1bsHEjXr6Eigp27KhlDVIoMjJy2LBh8fHxZmZm169fb1Nt37+M169f3759W+guQcJmxocPH3bv3g1g+vTpgtt2MzOzgwcP3rlzh5nVc+zYsX/++WfJkiW1vJKaI4Rs3rx5xYoVPB5v4sSJBw4cUKz2bzYlQZJ+xyWFOBz+jKPdu6spaW5OAPLDD2JVK+pN/atXRF6eAMTbu+Kur/dNfWhoKDOV87vvvuNwOGIeVWH55SoI3tTfuXNHX19fX1+/wvKuzF28np7etWvXBItqMAOe9u/fX89XW15WVhaTo1ZGRsbLy6tBz0XVC9obrTk1NXTqhJgYVJ3zhsvFq1cAYGlZp9PNno38fADw98elS+V2MS+UfXxw7RqcneHmhqwstGyJknQSUury5csTJkzIyckZPXr0qVOnatER69atm66urtBdjx8/LpvI2dbWlrmdL+vBgwd79+4FsGfPHiZ7940bN1asWBEWFgZg2bJlPB5vxowZDfSqJyoq6urVq61atTpz5syQIUMa4hRUPZN0HJdOc+cSgKirVzVu9OBB/rhR8VaxF9kbbdu24px9oV9LlhBCyIQJpFs3cvYskdoZ0MeOHWPeI02bNq2wsLBGx9Zu3GgFeXl5TKoOBweHCrvK5pHr0KGDj49P5bGr9eLChQtVTAegmhraG62V2bOxdy84HHh4oPyKdXwcDn9Y0nffwcysTue6ehWicpSNHo2PH7FgASZPho4OsrJw/z4SEjB+PKytsW4dqlwEpQny9vZetGgRj8dzd3f38vKSSBs8PT0jIyNlZGR++umncMFgtTKcnZ0fPnwYGxs7a9Ysb29vDw+P77//nlXdHUBRUdHjx4/j4+M5HE7r1q3NzMwMDQ1FFR4zZkyFLfHx8U+fPk1LS9PU1OzYsWO3bt1qcWlUQ5F0HJdaCxbwu4GrV5P8/HK7oqP5E+QVFMp1RefOJT/8QA4fFl6hmJNBy6r8bDQ/n/j4kDZt+G2zsSEBATWoUIJ4vAJ3d1tzczabvWvXrtrVUS+9USaVSbUmTZrUqVMn5nO3bt3Onj0rqsK0tLRFixZVXjvPysrqzp071V5UUFCQtbV1hWNNTU3FOZZqHDSM1lZeHhk3jh+t2rQhM2YQDw+ybBkZNozIyfFj6Pnz5Q7R1ycAmTu3dMu+fcTOjv/FpGtq27Z0S7WTmkW9YsrOJjt2EF1dfvP69iU3btTHNTeYwkIydSoBMlq3DhAdj6pVL2HUxsamlwjMvKkOHTr06tXryJEjBQUFPj4+giQpNjY2Nyr9nKOiojp27MgU0NDQGDFixKRJk+zs7BQUFACw2ezDov6sEkII+fPPP5l+rpKSkr29vZubm4uLS+fOnVVVVZOSkmr446EaCg2jdVBURPbs4acgKfvFYpEhQ8jTpxXLDx1KLCzI77+XbhGkiRL6Ve1zt6rf1HO5ZMcOoqNTGkyDgmp/sQ0nL4+MHUsAoqxM/vmnLjXVIozm5+cnJSUlJSWJ8xxW6Jz6/Px8Hx8fwRutvn37ClJAcbnczp07A5CVld2xY0fZU3z+/Hny5MksFmvVqlWiThccHMzE0JEjR34SpLAhhBAiyBZINQU0jNZZcTEJDSX795ONG8m2beTkSZKYKO6x8fHk0SORX9W+Jnrxgjx6RJKTqyqTlUW8vEirVvxgamdHQkPFbV4jSE8n/foRgGhokBpmZa6sFmH035KMB+Hh4dXWX0VqkqysLE9PTybJsZycXHJyMiFk3bp1TOVCD+HxeFWnkTY3Nwdgbm6eX+GpEdXE0DDaPHz5Qry8iLp6aTB9+FDSbSIkMZFYWBCAGBiQ16/rXp8EwygjIyNj9erVS5cuJYTweDxmPH+fPn1qfimEGVwF4MKFC7U4nGpM9E1986CiAnd3/PQTdu7Ejh24cQM3bsDODps2oWdPyTTp7VvY2+PtW5ia4vp11EfiDxaLZWVlhZJVmITq3Llzenq64O2QmZnZkSNHAHTo0KHa+rt3756Tk6OpqSmqgLq6+vr165nPUVFRzIjUKVOmVFtzcXExk5ReS0uLGVbFxHcFBYXhw4dXezglYZKO41SjS0sja9cSVVX+Y9yRI8XNiFqPwsJI69YEINbWJC2tsc/eKE6ePMn8ij0W48dbeWEPZpkQKysrQsibN2+2bNkyc+bMOXPmeHt7V3hOSkkcDaPNVWoqcXcniooEIGw2cXYm5ZeUaEA3bxIVFQKQkSOrmr8g5QT5SVNTU6stXDmM9unTB8DQoUNnzpxZIbOqoqLioUOHGrj5VA2wCCGN0+2lmqLUVGzdip07kZsLNhvjxuG332Bi0oBnPH8eEyciPx9TpuDgQTRY4juJ8/LyWr58OQAul8uMRZ02bVr79u0XL15ceQxpbm6uv78/ABsbG+bxQrdu3SIiIpi9/fv3d3R01NPTe//+vY+PT3x8PIvFunTp0ohaJ7uh6pek4zjVBMTHk19/JQoKBCBLlzbgiXg8YmdHAOLuLr3TVcW0a9cu5lcsJSWFEJKamspms5WUlPLy8sQ5nJmQ2rp168DAwLLbMzMzmelP5ubmDdJuquZovlEKaNsW3t54/Rpz5mDp0gY8EYuFv/+Gry+8vKQ9f0q1BG+imBdNgYGBPB7P1tZWXl5enMOZUQG9e/e2s7Mru11VVZVJ2ff8+fP4+Ph6bjRVK/RNPVXCwICfYJ8QREQgNhYZGVBTQ7t26N4d7Jr/xX30CJqa6NChXMRUVcWPP5Z+GxGBmBhkZ6NtW9jYQLwQIxUESabDwsJ69OgRGBgIQPyMTW3btg0NDY2Li6u8q0uXLsyHjx8/tmvXrj4aS9WNpLvDVFOSnU02bhQyL0tLi6xcScTM+5mfT86eJVZW/GMvXRJe7N49YmZW7ixqamTjxurnbkkJHo/HzBPt378/IeTSpUvTp08Xf1VkDw8PAMrKypXH3vv5+TG/vNHR0fXcaKpWaBilSiQn8zOqAERbm4wZQ9zcyPfflyY6MTEh795VVQOHQ3bsqJjZT2gYDQriZx5QUiJjx5KpU0mnTvzybm4NdH2NjwmFAM6cOVPTYwXD78+dO1dh1/jx4wFoaWkVFBTUU0upOqFhlCKEEFJURPr2JQCRlyd795brEvJ45MgRoqREAPLNNyQnR2Qlkyfzc7IMH0727BEZRnNzSfv2BCDGxiQ2trQBP//MP+Tq1fq+vEZy9OhRGxsbR0dH5tusrCwjIyMA8vLyBw4cKC4ufvPmjY2NjY2NTXR09IcPHzZs2MB0NjMyMiZPnjx58uR79+4xx165coWZWspms9u3bz9jxoynT58SQv5kHrwAy5Ytk9RlUhXQMEoRQgjZu5cfwvz8hBe4do2/dIqHh8hKnj4l586RrCxCCCkoEBlG9+/n7woJKbe9sJB/m29rW5dLkaCNGzcCaNOmjWDLixcvBCmgOnToMHr0aOZz9+7dmdGgfn5+pPy40aKiIqErgLLZbAMDA+Zzr169cqr4e0Y1LhpGKUIIIaamBCBDhlRVZvx4AhBdXSLOvWQVYdTBgQDEykrIUbt28acDSGcWuMphlBCSmJg4c+ZMOTm5CmGxRYsWDg4OT548IYSkpKR07dq1a9euAQEBK1euZApMmDDh5MmTTIISAVlZWVdX1yzmbxXVNNAwShHy4QM/5Pn6VlXsyhV+sbAwQggpLCQfP5KPH4nQgZBVhFEms6rQAaoREfyjpCXbdHlCwyiDy+Veu3ZtLbMmArBv37709PTKxdLS0phUpOPHjxdsfPbs2cGDB/X09ADYSm1X/StGx41SgGCpjKpXQhfkYGfW8ouOhr4+9PVRkiRJLCkpSE8HgJJb3XIECUqiompQpzRQVla2t7dnlvwEYGVlxTz6rOCff/7Jy8sDsHr1asFGc3PzGTNmMElP7t+/n8QsZUg1GTSMUsCnT/wPQkObgKYmFBQAICWl9uficPgftLWF7FVT4w8dLb+a/Nenf//+ycnJlbczA0Xl5OS6du1aYVe/fv0A8Hi80NDQRmghJT4aRikgO5v/oczKl8K1bAkAXC4A6Ovj6FEcPVqzVHu5ufwPleaV86mrA0BOTg3qlELZ2dm3b98GsGzZMicnJ8H6fcwdfWFhIZf5IZfRvn17Jhn+69evG7exVDXoLCaqJDgCyMmBikpVJZnfbVVVAFBTgxiZNCti+rMAMjOFF2C6q+KtK9c05ebmCrLkVVB2+mZQUNCECRNu37794MEDUpIhyMLCAgAh5MqVKz/88EPZY7lcroKCQm5uLkfQo6eaBhpGKaB1a/6Hjx9RMtFQiPR05OWVK18LggeCQp8MZGYiPx8o6ZNKp4yMjEmTJlVb7NatWwDMzMwIIcySTQAGDRpkYmISHR09f/58FRWV4cOHs1isrKyss2fPrl27Njc3F0CuoEdPNQ00jFIovSsPD68qjJbMq4GlZe3Ppa2NVq2QkQGh70kSE/kfqmhGk6ekpMRMNKosIyPj4sWLAFRUVGJiYhISEvbv31+2AJvNPnny5ODBg1NSUkaOHKmsrKyhoZGcnFxYWNi5c2dFRcXc3NyWgrsHqomQ8EgBqokwMeHnUa7C1KkEIDo6dR03am9PAGJjI+QoZhbA1zVutKxnz54xv3f9+/cHcOzYMaHF3r17N2nSJKWSR9VGRkbr16/PyMhgs9kA/vjjjwa7Aqo26CsmCgAwZw4AXLmCmzeFFwgPx4kTADBrVl1zLTs5AcCDB3j6tNx2Hg8HDgBAv34oWa/4a2VpaQkgKChI6N6OHTseP348Ozs7MTExMzPzzZs3q1evTkpK4vF4ACq/xKcki4ZRCgAwZw4sLEAIxo3DlSsV9965g+HDUVQEI6PShKSJiZg+HdOno6SHJa4pU9CmDQjBjz+WPiElBKtW8UewurvX5VKkArP0nqgwKqCnp6fKvNADgoODAcjIyPTq1auBW0fVDH02SgEA5OVx/jyGDsXbtxg5ElZW+O47aGqCw8G9e7h/H4RAXx/+/qWv9TkcHDkCAGPHwsKCv3HGDDAvowWL06xeDW9vALC0xO+/A4CSEg4dwsiRePEC33wDJyeoquL2bTx+DADTp6MZrIVpYmKiqan5/v372NhYcRYlLS4u9vHxATB48GBtoUNuKcmhYZQqYWiIBw+wejUOHkRYWOkLJQAyMpg8GRs3ok2b0o3y8mDeL5cdIxUSggqjGgV37mXXZbO3x9WrcHNDbCwOHiytcMkSlEyX/Lqx2WxbW1t/f/9Tp04NGTKkVatWTC4oUdatW/fs2TMWi+XeDLrqUoeGUaoMLS3s3YtNm3DrFt6/R1oaNDRgYIBBg6ChUbGwkVHFiAng0KHSwfwVVFjefcgQvH2L//5DVBSys9G+PQYOFDkm/2s0cOBAf3//7du3r1ixYvTo0cySdgA+ffrk7u7+/fffm5iYsFisyMjI/fv3X7lyBcDcuXMHDRok0VZTQtAwSlWiqooxY2p5rI1NDQqz2ejXD/361fJcUm7gwIEAMitNQ/Dw8PD19fX19S27UVZWdsmSJZ6eno3XPkpsNIxSVL3p3LnzmDFjNCv0u8tQU1MbM2YMAHV1dQMDA21t7ZSUlKNHj1oIHi4DXl5eXbp0uXnzZlxcXFFRka6urq2trYuLi2CIPtXU0HXqKUpinJ2d/fz89u/fP3PmTEm3hao9OuCJoiSGedDJjGSipBftjVKUxCQnJ798+bJPnz6KioqSbgtVezSMUlRjy8nJSU9PZ7FY+iISvPJ4vMTERACtW7eWZxKwlsfhcNKZ7NeitW/fvkUL+vKjMdCfMkU1ttOnT7u6usrIyBQVFQktkJKS0q5dOwDXr18fOnRo5QL79u1bvnx51WeJi4tr37593VtLVYuGUYqSPurq6oaGhkJ3cbnclJQUAEK7sVRDoGGUoqTP7NmzZ8+eLXTXzJkzDx48OGTIEB0dnUZuVbNF39RT1NcjJSXlxIkTABYvXizptjQjNIxS1Ndj165deXl5ZmZmQp+oUg2E3tRTlBQoLi7m8XhsNlumbIaX8nJycvbt2wdg0aJFzOJ3VOOgvVGKkgKOjo5ycnLMNHxRjhw5kpaWpq2t7eLi0mgNo0B7oxQlKTweb4yIFDD5zLp+Naxt165dAH799VcFwfKrVKOgYZSiJIMQwixvJ46ffvpp8ODBbdu2FVXg4sWLr1+/VlJSmjVrVj01kBIXDaMUJRlsNvvy5ctCd1VeollUv1Vg69atAKZNm6alpVVfLaTERMMoRUkGi8VycHAQuis5OblGVYWFhd2/f5/NZs+bN68+mkbVDH3FRFFS748//gDg6OjYpUsXSbelOaJhlKKkW2xs7IULFwAsWrRI0m1ppmgYpSjptn379qKiIktLS1tbW0m3pZmiz0YpSgocPnz41atXhoaGFabSZ2RkHDp0CMCSJUsk1DSK9kYpShr4+fn98ccfJ0+erLDdx8eHy+UaGBiMGzdOIg2jQMMoRUkFVVVVDQ0NtfILUBcWFu7ZswfA/PnzaYZmCaLZ7ymqsb179y40NJTNZk+YMEFogby8POat0cCBA3V1dUXV8/nz51u3bgFwcHBo2bJlA7WWqhYNoxRFUXVCb+opiqLqhIZRiqKoOqFhlKIoqk5oGKUoiqoTGkYpiqLqhIZRiqKoOqFhlKIoqk5oGKUoiqqT/wOht7tB0nCxfwAAAVN6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wMy4zAAB4nHu/b+09BiDgZYAAJiDmg/IbGNk0FIA0iwSQoQJkMLJIMDFqaIBYjBLMcBYLB1gZkwQrTD0bXI4dzuKAszhh6rngQtwwIR64EC+HxgIIi4+bEewwZqDRDAysjAxsrAzsrAwcrAycjAyMXCxM3CwMPNwMvJwMIiAviG8CaYNiBr4VQY/3H3vIY2+zIGZ/bMD7/WqSO20nm4oc4NdI3v/YbJp96E3z/T/u39r/Qnjmvlol/X0V3FYHIu+us//RwbFfo05k/4og4f03bQ0PvIretm9p9q79P0W4DrDHd9i9uPnG9vJ83QM1O3T219rn7p8XV3yg/LPVgSj9uP2rtsscmLyS7UDIsXx7ZjW7A36uFQeyhL/bLcg5ZVv3uPeA99l1do+sDfefiGs9YPIlf1+kieYBMQA2nWFcrKwdYgAAAdd6VFh0TU9MIHJka2l0IDIwMjMuMDMuMwAAeJx9k0ty4zAMRPc6BS8QFZsAQWKRhW3lMzUTqyrj5A6zn/tXADKU5E1kLyjWI9BEt6bgz/vy+9//sD20TFMI8Ye/qoZPijFOb8EX4fz08usaLrfTeexc1o/r7W8AB5Cdsd89e7qtb2MH4RIeMJeSVQycc46UYniIcxVUW2GQqZNVVNjJqJGqkWkm5FIa8PzyuPEUXluZWsj7zChkxX2roMKa0SDZSdNgd5MGJK1a2woF0YmB5rB6R42i2YuSCKiRmYhMVx6kuFzbrjH2rrlk7nqLKEcjBlq6UsoCu4Yp4SyMhtrNshMDrYaaPhHKPgMgS+41kapVrwPUXlMKi7b2mqQ4SnONyQ/pZkH8vlQC8D36or0/c7UJGLLB8MImLJpCl6A2gtRYNbI4sbHJC1vDoomc5Zpy6d7Vyg5saHOLZ7YZJieIwV2M2GHPwWaX5Wp1FlJqj0oido9NgzC10HB4Py2PKXye/uyJeLoud9nraTyv12VPIyxGh8jBYnRIFCwr2GMDywP2aMBMT7v/MGMPHsPMk91HmEWyuwW3IR0saULQIk2H4cOnmg8Ths/ubpBoX1483vp4R38fX6utpy8tPMJyOt3NtQAAAX96VFh0U01JTEVTIHJka2l0IDIwMjMuMDMuMwAAeJxtUrtuHDEM/JWUPmBPEN+ktrHhFGkSpzc2gA2ky+EMI4Ebf3xIXbnb6DEzHI4EPj8O2O6eH+/vB9Y+NNdvw7ZTbb6dco26PA3oW608MYCbgm5bgU9DpgC34mn78nl3hmYmoUtvIp1wOffmCo5rMq6hnEyPTr6csRGI2ZoS8d6ltGLCkzINLoZEwZYsZlGGYrJGaO0NVUnSDUBUZgmge9WosUa5BaolRc07qqypCQSAWyiLacfsBuuZG6j5zIvEREUpUwXPlj1bpx2pYhFh0Gdud0spNDCSmd/AQeut1iN/odzCw+sAWUQzQ9eQLEo3oPloIsrHZkwLpGzEjmLzo9y5sjFxxwSIgWd8TSGell8wbHn5e738fL++jd4u1z8Pefv+8vbj3+X193uDAXsQB+5BHbQHbfAe9CF7MIYedO/DDlAefoDCiD1KOYR7lHMa96jkJB74Yo7mAUzLx9f8HPv8D23c0CwWZJD3AAAAAElFTkSuQmCC",
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AXFxcANC1USKgTnsvgnRuly9f5hZmHTZsGAADAwNjY+O6ujqZywwSojTUG+WJtbX1W2+91cbCGXp6em+99RYAU1NTHutSV/Pmzdu3bx/+ilEAFhYWVVVVYrG4eSnSzs3JyUkkErX6VlVVFc/FaDiKUZ4sWbJkyZIlbTTo2bMnLTzccU1NTdxt+iFDhnCvWFhY5OXlicXiPn36CFkZXygrVQed1BP109jYOH/+fABaWlpubm7btm1ramrStMujN27cqJThjTfeELo6zUK9UaJmqqqqvLy8YmJijI2Ne/funZubu3Dhwk2bNnXr1g2aFKOGhobc8oAvknWyT5SEeqNEnZSVlb333nsxMTE9evQ4d+7cvXv3oqKiHBwcsrKyEhMTAcTFxdHDbgnPKEaJ2vjzzz/d3d2Tk5Pt7OwSExOdnZ21tLS8vb2zsrIiIiLMzMwAREREjBw58vjx40IXSzQIxShRDzdu3BgxYsS1a9cGDhyYmJjo4ODQ/Jauru7s2bO//fZbAEZGRikpKVOnTnV1dU1ISBCsXKJJKEZVT0YGAgMxaBC6doVIBHNzuLjg66+Rlyd0ZYJJTU0dM2ZMfn6+m5tbUlKSjY3Ni22srKwATJgwYcOGDd27dz9//vzYsWPHjx/P3dDvBO7fvy90CUQGRlRHQwNbvJhpaTGglT/6+iw0VOgSBRAbG8sNBf3www+rq6tlNauqqiorK+O2KyoqQkNDm59u7+npmZ6ezle9isc9VMbMzOzWrVsxMTHcl8rNzZXV3t7eHsBXX33FZ5GajGJUlcycKU3MiRPZyZPs8WPGGCsuZlFRbMgQ6VurVgldJa9+/vlnXV1dAJ999llDQ0PLt4qLi5OSkpKSkiQSSaufLS0t9ff3525nc1dRc3JyeKlakfLz87kpBqampqdOnaIYVUEUoypjzx5pUK5Y0cq79fVs+nQGMC0tlpTEe3HC2LBhg5aWFoCgoKAXs3LPnj1coNTX18vag5GREYBx48YZGho2h+mdO3eUXLjCJCcn9+zZE4C9vf21a9cYY0+ePElLS0tLS6urq5P1qaysrLS0tIKCAh4r1WgUo6qhqYn17csA5urKZPStWHk5s7ZmAJs8md/iBCCRSIKDgwGIRKJ169a12qbjMbp+/fr8/PzAwEB9fX0Aurq6AQEBDx48UFr5irF3714u/ceMGVNcXCx0OUQmilHVkJIi7YoePNhWs5AQaYdULOarMgE0T1LS09Pbt2+frGYvFaPcj7m5uQEBATo6OtzOAwICCgsLFf8F5NbY2BgUFMR9u4CAgDa+IFEFFKOq4YcfGMBEIvbXTZLWJSdL0/b33/mqjG+1tbUfffQRAGNj4+jo6DZavkKMcm7cuOHn58ctUWpsbBwUFFTW9l87v0pLSz09PQHo6+vv3LlT6HJI+2jAkzDq6uqOHz/OrdkOAPfuAYCVFczM2vqYo6N04+5dZVYnmEePHnl6eh4+fLhbt26xsbETJ05UxlH69+8fGRmZkZHh7e1dXV0dFhZma2u7fPny8vJyZRzupdy6dWvUqFFxcXGWlpYxMTHz5s0TuiLSPopRYaxcuXLq1Kk+Pj7Sn7k87dq1nY916QJt7aftO5fCwsKxY8cmJSXZ2tomJye/++67Sj2ck5NTVFRUSkrKBx98UFFRERYWZm9vHxYWVlNTo9TjtuHEiRPDhw+/efPmkCFDLl265ObmJlQl5KVQjAqjR48ezf8LQPosz45MBufadLpnf969e3f06NEZGRmOjo5JSUn9+vXj57jDhw8/fvw4N1a/tLR0+fLltra2YWFhtbW1/BTAYYyFhYVNmTKlvLz8k08+OX/+vK2tLZ8FEHnQCk/CWLp06cyZM62traU/cwPFHz9u52Pl5ZBInrbvNFJTJatW5d2/7+rqeuzYMbO2r2y8YNKkSbLWNKqrq+vIHkaOHHn69Om4uLhvvvnm0qVLy5cv//HHH1euXDlv3jzufpRS1dbWLliwYO/evSKRKDg4ODg4mJZoUjNCX5wljDHGNm6U3jtq+xZ8UpK02alTfFWmfDExzMSEAVe/+KKNSUovar7F1K7nbjG1LTY21tnZmftgnz59wsPDGxsbX/5bdVTL0fVHjhxR3oGI8lBvVDWMGCHdOHMGH38ss1lcHABoa8PFhY+qeHD4MHx9UVcHP7/BP/wAXd1X2Ed0dDR32/1FU6dOfdnTc09Pz8uXLx88eHDVqlU5OTkLFy7cuHFjSEjIxx9/rPBO4vnz5728vB4+fPjmm28ePXrUsfkWIlEvQuc4YYwxJpFIh9+PGSNz+H1lJevViwFs2jTpK19/zQICWH4+b2Uq2JYt0gUEAgNlfmvZXnnAUwc1NTVFRUW9+eab3FEGDRoUFRX1CvuRZdu2bXp6egAmTJigUiOuyMuiGFUZu3ZJT9jXrGnl3aYm5ufHAKatzS5cYIyxJ0+YqSkDmIEBCwxkKjmMvC2hodKhsmFhr7YDZccop76+Pjw8vHlNqREjRnArQ8ujoaGh5ej659YKIGqHYlSVTJsmTVIfH3b5srSD1tDATp9m7u6tzLjPzmZ+fkxbW7r+U0AA+/NPoWp/CY2NbOFC6a+EHTteeTf8xCinrq4uPDycm94OYNSoUQkJCa+2K7FYPG7cOAD6+vq7d++WszCiCihGVUldHZs9++nKeAYGrHt3pqsr/VFHh4WEtPKprCzm5yc9OzY2ZoGBrKiI99I7rLaWeXszgBkZsd9+k2dPfMYop7KyklvMlDsudxX1pfaQmZlpZ2cHwNraOiUlRSFVEcFRjKqec+eYry/r3v1pnvbpwxYuZNnZbX3q2jXm7c1EImmYBgWx0lK+Ku6wigo2fjwDmJmZ/OtU8R+jHG4x065duwIQiUQffPBBBxczPX78eJcuXQA4Ozvn5eUpsCQiLIpRFVZTw8Ri9lLLUmRkPA1TExMWFNTOJH0+FRayt99mAOvZk2VkyL8/oWKUIxaLg4ODTU1N0YHFTCUSSWhoKLfon4+Pz0uN6yKqj2K0M0pJYR98IO3JmpqyoCDpCtACunePOTgwgPXvzxTUERM2RjnFxcVBQUEGBgYAdHR0/Pz87t69+1ybiooKLy8vANra2qEa+fyCTo9itPNKTn4apubmLDiYlZcLU0lmpnSlVBcXprh1M2/evLlly5YtW7Y0NTXJahMeHr5ly5YMRXR+2/DHH380L2bKrb/XvGTyH3/8MXToUABdunQ5duyYUssgQqEY7eySkpiHhzRMLSxYaCirquK1gDNnWNeuDGDjxrEnT3g9NL+4xUy5iQD6+voBAQFHjx7l7kc5ODhkt31pm6gzilHNkJj4dMiUpSULDWX8XJ47coQZGDCA+fq+3EVetZWVldU834k72Z88eXK5UOcBhBcUo5okNpYNHy4N0+7dWWgoq6lR4uF272Y6OgxgX3zBZJ93d0pXrlyxsrLS1dX19fVt45oD6Rw623prpC2enrh4EbGxcHFBcTGWL0ffvti4ER1bBunlhIVh7lw0NSE4GJs3d76V/dr21ltv1dXVNTQ09O/fX0vDvrsGov/AmsfTE6mpiI2FszPy8/H3vys+TPfswfLl0NbG9u0ICVHYbtXHpUuXHj16ZGpqevLkSaFrIUonYh1ZKph0Sozht98QHIz0dACwtcU332DePLSxwmZVFeLikJiIhw9RVwdzczg6YuJEODg806y+Hl5emD8fH36o3K+gqv7zn/8sXbp00KBBubm50dHRo0aNEroiokxCX1UgQmtqYlFRrH9/6TVTOzsWHs5aXWFz61Zmafl0blXzH5GIeXurx3R+HmVnZ69YsQLAhx9+KHQtRLmoN0oAABIJDh3CypW4dQsABgzA8uWYNQvN63gGBmLzZgDo3Rs+Phg0CPr6ePAAJ05IV0Ht0wfx8XjjDYG+gAAqKioKCwsBODg4tLoUaXR09OTJk3v27HnmzJm+ffvyXiDhi9A5TlRJQwPbtYvZ2Um7mXPnSl/fs+fpKy+OlDp2jBkZMYANH64ho5o4ERER3P+JamtrW22QmZnJxevo0aN5ro3wiW4xkRZ0dDB3LnJyEBEBe3t8+ikA1NWBWxzT3R07d8LQ8PlPTZmCn34CgNRU7N/Pb8UqzcrKitu4fft2Q0ODsMUQ5aEYJS/Q1cXs2bhxAx4eAHD0KB4+BIDvvoOsp2j4+WHgQADYto2vKtWAhYUF1xstKSnZvXu30OUQZaEYJTI0Pxbp7FkA6N4dbd9u5h4hdfEihHvOu6ppvmDa1NQUGRkpbDFEeShGSXuuXQOAwYNldkU53NM0Gxpw8yYfVamJ5iTNyspKSUkRthiiJBSjpD2lpQBgadlOs+YGXHvyrMePH3///fdCV0GUgmKUtIeb3aSn104zAwPpBp3Ut9ByIFRaWlpxcbGAxRAloefUk/Z07QoAFRXtNHvyRLrx2mvKrUf1zJkzR9bE+aampubtP/74Y+3atevXr+erLsITilHSHu5xmLm57TS7f1+68deziDXHgQMHOtgyPj6+sbFRp43ptkQN0X9O0h4XF/z+O65fR2UlTExkNktOBgBLS9jZ8VaaioiMjJSVjHPmzGk5YvTmzZsRERH+/v58lUb4QJNBSXvS0jBsGABs347581tvU1kJW1uUlWHePOzcyWd1AoqMjJwzZw6A2tpa7gkiLzIyMqp59mLx6NGjz507x0d9hC90i4m0Z+hQ6YjRVauk4/BfFBSEsjJoaSEwkM/SVN+Lc+2vX79+5coVQYohSkIxSjogPBwGBigqgpsbLl585q0nT7BoEbZuBYD/+R8MHixIgSrrxVtPjx49WrNmjSDFECWha6OkA5yccPgwvL2Rk4MRIzB4MAYPlq7wdPYsqqsBYPZsrF0rdKHqIS0trbS01NzcXOhCiGJQb5R0zPvvIz0d3t7Q00NGBiIjsX07oqNRXY3+/fHLL9iz5+n8UdKm3Nxc6pB2JtQbJR3m4ICoKFRW4sIFFBWhthaWlhg4EG++KXRl6ic2NrapqUm7eTlXos4oRslLMjHB+PFCF6H2bt68uXfvXu5GP1F32iEa+cQxQuQnEomsrKzc3d3d3d1lzWLS0tIqKioqKSlp+aKJiUmfPn3Mzc09PT15qZQoF40bJUS5Fi1a9NNPPxkYGNjY2PTq1cvJyWnGjBkjR47UpUvJnQWd1BOiXDNmzNDR0fH19XVxcaFpoJ0S9UYJIUQu9LuREOVqaGi4f/8+gN69exu++CQrAEBBQUFVVZWpqWlPbiEYolZo3CghypWfn9+vX79+/fpduHBBVht/f/9+/fp98cUXfBZGFIVilBBC5EIxSgghcqEYJYQQuVCMEkKIXChGCSFELhSjhBAiFxo3SghP1qxZs1PGE1YyMzN5LoYoEMUoITyJj48XugSiFBSjhPBkxYoVTk5Orb4VGhpKHVL1RTFKCE88PDw8PDxafSsiIoJiVH3RLSZCCJELxSghhMiFYpQQQuRCMUoIIXKhGCWEELlQjBJCiFxowBMhymVmZhYcHAzAzs5OVptPP/10xIgRjo6OPNZFFIaexUQIIXKhk3pCCJELxSghhMiFYpQQQuRCMUoIIXKhGCWEELlQjBJCiFz+HwU3J7/f+f9GAAABVHpUWHRyZGtpdFBLTCByZGtpdCAyMDIzLjA5LjYAAHice79v7T0GIOBnQAA+IOYF4gZGNgMVIM3EIgniOpbk54JwZrJfaa6iFBtUtQyDNpsCSBkjC6OCBohmhNEcCgog7RBpJgxpNGU4uQvANDcDI8gQJgZmBgYWBkZWBkZOBkYuFiYWbgZWNhZGVnYGVg4GTl4Gbh4GEZC7xDeBtEExA9+KoMf7jz3ksbdZELM/NuD9fjXJnbaTTUUO8Gsk739sNs0+9Kb5/h/3b+1/ITxzX62S/r4KbqsDkXfX2f/o4NivUSeyf0WQ8P6btoYHXkVv27c0e9f+nyJcB9jjO+xe3Hxje3m+7oGaHTr7a+1z98+LKz5Q/tnqQJR+3P5V22UOTF7JdiDkWL49s5rdAT/XigNZwt/tFuScsq173HvA++w6u0fWhvtPxLUeMPmSvy/SRPOAGADZTGUmK4UFYgAAAcp6VFh0TU9MIHJka2l0IDIwMjMuMDkuNgAAeJx9k0tu4zAMhvc+hS5QQRQpPhZdJHEfg5kmQCftHWY/90dJqa4VFKidhUl8JH8+sqR4Xtff//6nrwfXZUmp/PAzs/SOpZTlJcVHOj48/Tqn0/Vw3Dyny9v5+jcBJUCP8feWPVwvL5sH0indQRZpxg7m1grWku5KVgatEX16fLqvG18Hr2xMwRcrqM7XjNBENh42HtNzT6aCUS2DoJcIl4ACT9ooSFfiHXIHqqlp/wKBghPa0iUqWmFrkRSZATvZEJEmkkOuu7WUUbVJo6FX2Kh860+GXmwM3ozrocYEPcD7a7MKddRVMmOLSQA0biMzVNUJtJGThdi6CKssgWLWUiPoC/VKo7UKAJ9rEBv1idTnMLEQeV1XcYGhwHwOtaPmoMxojbReTqxGC5m0Nhn7U50HBn1hlMnHGNvPSEBDCXtsnVGKpJSBRcfJVKTYsgtgwnE8r4f1vqb3w5/9JB7O680JjqM8Xs7rfpTxTjcXJvZF4X5Y4aT9esJs+4V4fOL9DMKEsm+523CbUiODz2rfG3ik3B40lO7VaQth2zRq6BBNI4XuxnkCc79hb39g/14+ABKZxsII9U1UAAABGXpUWHRTTUlMRVMgcmRraXQgMjAyMy4wOS42AAB4nE2QOW7DMBBFr5LSAihi9sVqAjiFO1WpBOUabnz4DKnGjTjk05955PE89/1xO57nMj/H4/u8dvuxn8sneZ5f79sqXViAGnQWFGwrdEti2lbu4VUVkSD1WiEjZFupJ1hqw85myCOizFwEu7um1a+qwDRIGEY1qyKcuTLorDqIY6DNDGRl6oQyMkaBjsADhaXJnAxchDqjug8FhJKFnqVAA2RFJkhCxGuyJw4kEo5b3RTNY0oRS7kUMuHLTgNgWqmrzEluKYOwGnorF1GT2a8UlLfqYsZaDohqOiNIESNjLpajW5J5oXpKINOl/eE92+v3jpWC18+dOzfr9v4HrMlbcnCe4iAAAAAASUVORK5CYII=",
       "text/plain": [
-       "<rdmc.mol.RDKitMol at 0x14f09aa10>"
+       "<rdmc.mol.rdkitmol.RDKitMol at 0x7f3a0e78a250>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "RDKitMol.FromXYZ(xyz,\n",
-    "                 backend='jensen',\n",
-    "                 header=True,\n",
-    "                 allow_charged_fragments=False,  # radical => False\n",
-    "                 use_graph=True,  # Use networkx to accelerate (enabled by default)\n",
-    "                 use_huckel=True,  # Use extended Huckel bond orders to locate bonds\n",
-    "                 embed_chiral=True,  # Embed chiral information\n",
-    "                 force_rdmc=False,  # use RDMC's implementation of xyz2mol\n",
-    "                 )"
+    "Mol.FromXYZ(\n",
+    "    xyz,\n",
+    "    backend='xyz2mol',\n",
+    "    header=True,\n",
+    "    allow_charged_fragments=False,  # radical => False\n",
+    "    use_graph=True,  # Use networkx to accelerate (enabled by default)\n",
+    "    use_huckel=True,  # Use extended Huckel bond orders to locate bonds\n",
+    "    embed_chiral=True,  # Embed chiral information\n",
+    "    force_rdmc=True,  # use RDMC's implementation of xyz2mol\n",
+    ")"
    ]
   },
   {
@@ -207,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -233,8 +234,8 @@
     }
    ],
    "source": [
-    "mol = RDKitMol.FromXYZ(xyz)\n",
-    "print(f'xyz coordinate: \\n{mol.GetPositions(id=0)}')"
+    "mol = Mol.FromXYZ(xyz)\n",
+    "print(f'xyz coordinate: \\n{mol.GetPositions(confId=0)}')"
    ]
   },
   {
@@ -246,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +267,8 @@
       "H     -2.104100    0.936620   -2.971070\n",
       "O     -3.879230    0.482570    0.098840\n",
       "H     -4.434020    0.341410   -0.692320\n",
-      "O     -4.167820   -0.234330   -2.643820\n"
+      "O     -4.167820   -0.234330   -2.643820\n",
+      "\n"
      ]
     }
    ],
@@ -302,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/ipython/notebooks_to_clean/TS Conformer Generation Workflow.ipynb b/ipython/notebooks_to_clean/TS Conformer Generation Workflow.ipynb
new file mode 100644
index 00000000..342c71a4
--- /dev/null
+++ b/ipython/notebooks_to_clean/TS Conformer Generation Workflow.ipynb	
@@ -0,0 +1,4025 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[12:22:52] WARNING: not removing hydrogen atom without neighbors\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "from rdkit import Chem\n",
+    "from rdmc import Mol, Reaction\n",
+    "\n",
+    "from rdmc.conformer_generation.ts_generators import TSConformerGenerator\n",
+    "from rdmc.conformer_generation.ts_guessers import TSEGNNGuesser, RMSDPPGuesser, AutoNEBGuesser, TSGCNGuesser\n",
+    "from rdmc.conformer_generation.ts_optimizers import SellaOptimizer, OrcaOptimizer, GaussianOptimizer\n",
+    "from rdmc.conformer_generation.ts_verifiers import XTBFrequencyVerifier, OrcaIRCVerifier\n",
+    "from rdmc.conformer_generation.pruners import CRESTPruner, TorsionPruner\n",
+    "\n",
+    "from rdtools.view import interactive_conformer_viewer\n",
+    "# from rdmc.external.xtb_tools.opt import run_xtb_calc\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ArgumentError",
+     "evalue": "Python argument types in\n    RWMol.__init__(RDKitMol, NoneType)\ndid not match C++ signature:\n    __init__(_object* self, RDKit::ROMol mol, bool quickCopy=False, int confId=-1)\n    __init__(_object* self, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > pklString, unsigned int propertyFlags)\n    __init__(_object* self, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > pklString)\n    __init__(_object* self)\n    __init__(_object* self, RDKit::ROMol m)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mMol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMolFromSDFFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/home/xiaorui/scratch/Gau-3140007.EIn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Apps/RDMC/rdmc/mol/transform.py:287\u001b[0m, in \u001b[0;36mMolFromMixin.MolFromSDFFile\u001b[0;34m(cls, path, removeHs, sanitize, sameMol)\u001b[0m\n\u001b[1;32m    284\u001b[0m suppl \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mSDMolSupplier(path, removeHs\u001b[38;5;241m=\u001b[39mremoveHs, sanitize\u001b[38;5;241m=\u001b[39msanitize)\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sameMol:\n\u001b[0;32m--> 287\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msuppl\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    289\u001b[0m new_mol \u001b[38;5;241m=\u001b[39m copy(suppl[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m suppl:\n",
+      "File \u001b[0;32m~/Apps/RDMC/rdmc/mol/transform.py:287\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    284\u001b[0m suppl \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mSDMolSupplier(path, removeHs\u001b[38;5;241m=\u001b[39mremoveHs, sanitize\u001b[38;5;241m=\u001b[39msanitize)\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sameMol:\n\u001b[0;32m--> 287\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m suppl]\n\u001b[1;32m    289\u001b[0m new_mol \u001b[38;5;241m=\u001b[39m copy(suppl[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m suppl:\n",
+      "\u001b[0;31mArgumentError\u001b[0m: Python argument types in\n    RWMol.__init__(RDKitMol, NoneType)\ndid not match C++ signature:\n    __init__(_object* self, RDKit::ROMol mol, bool quickCopy=False, int confId=-1)\n    __init__(_object* self, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > pklString, unsigned int propertyFlags)\n    __init__(_object* self, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > pklString)\n    __init__(_object* self)\n    __init__(_object* self, RDKit::ROMol m)"
+     ]
+    }
+   ],
+   "source": [
+    "Mol.MolFromSDFFile(\"/home/xiaorui/scratch/Gau-3140007.EIn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt1 = GaussianOptimizer(track_stats=True)\n",
+    "opt1.optimize_ts = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mol = Mol.FromSmiles(\"CC\")\n",
+    "mol.EmbedMultipleConfs(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "\n",
+      "C      0.749910    0.005816   -0.058451\n",
+      "C     -0.736625    0.007027    0.038979\n",
+      "H      1.259132   -0.392965    0.857771\n",
+      "H      1.064340   -0.637787   -0.887571\n",
+      "H      1.092689    1.054550   -0.137005\n",
+      "H     -1.156472    0.990945   -0.285009\n",
+      "H     -1.228044   -0.769937   -0.606496\n",
+      "H     -1.044928   -0.257649    1.077782\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mol.ToXYZ())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run into error in conformer optimization.\n",
+      "Run into error in conformer optimization.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# opt.work_dir = Path(\"test_conformer\").absolute()\n",
+    "opt_mol = opt1(mol, save_dir=\"/home/xiaorui/test_rdmc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024/03/28 11:24:47 AM | cclib | INFO: Identified logfile to be in ORCA format\n"
+     ]
+    }
+   ],
+   "source": [
+    "# opt.work_dir = Path(\"test_conformer\").absolute()\n",
+    "opt_mol = opt2(mol, save_dir=\"test_conformer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdmc.conformer_generation.verifiers.orca import OrcaFreqVerifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq = OrcaFreqVerifier(method=\"AM1\", nprocs=1, memory=16, track_stats=True, binary_path=\"/home/xiaorui/Apps/orca_5_0_4_linux_x86-64_shared_openmpi411/orca\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delattr(opt_mol, \"KeepIDs\")\n",
+    "delattr(opt_mol, \"frequency\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024/03/28 11:26:13 AM | cclib | INFO: Identified logfile to be in ORCA format\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
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+       "<table><tr><td colspan=\"2\" style=\"text-align: center;\"><image src=\"data:image/png;base64,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\"></td></tr>\n",
+       "<tr><th style=\"text-align: right\">Energy</th><td style=\"text-align: left\">-4225.401110340381</td></tr></table>"
+      ],
+      "text/plain": [
+       "<rdmc.mol.rdkitmol.RDKitMol at 0x7f9b71896480>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq(opt_mol, save_dir=\"test_conformer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: array([1348.14, 1363.43, 1384.77, 1395.37, 1414.74, 3104.11, 3136.26,\n",
+       "        3179.21, 3257.89])}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt_mol.frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specify reaction\n",
+    "\n",
+    "rxn_smiles = '[C:11]1([H:23])([H:24])[C:12]([H:25])([H:26])[C:13]([H:27])([H:28])[C:14]([H:29])=[C:15]([H:30])[C:16]1([H:31])[H:32].[c:1]1([H:17])[c:2]([H:18])[c:3]([H:19])[c:4]([C:7](=[O:8])[O:9][O:10][H:22])[c:5]([H:20])[c:6]1[H:21]>>[C:11]1([H:23])([H:24])[C:12]([H:25])([H:26])[C:13]([H:27])([H:28])[C:14]2([H:29])[O:10][C:15]2([H:30])[C:16]1([H:31])[H:32].[c:1]1([H:17])[c:2]([H:18])[c:3]([H:19])[c:4]([C:7]([O:8][H:22])=[O:9])[c:5]([H:20])[c:6]1[H:21]'\n",
+    "# rxn_smiles = '[C:1]([C:2]([C:3]([H:20])([H:21])[H:22])([O:4])[C:5]([H:23])([H:24])[H:25])([H:17])([H:18])[H:19].[C:6]([C:7]([C:8]([H:29])([H:30])[H:31])([C:9]([H:32])([H:33])[H:34])[c:10]1[c:11]([H:35])[c:12]([H:36])[c:13]([O:14][H:37])[c:15]([H:38])[c:16]1[H:39])([H:26])([H:27])[H:28]>>[C:1]([C:2]([C:3]([H:20])([H:21])[H:22])([O:4][H:37])[C:5]([H:23])([H:24])[H:25])([H:17])([H:18])[H:19].[C:6]([C:7]([C:8]([H:29])([H:30])[H:31])([C:9]([H:32])([H:33])[H:34])[c:10]1[c:11]([H:35])[c:12]([H:36])[c:13]([O:14])[c:15]([H:38])[c:16]1[H:39])([H:26])([H:27])[H:28]'\n",
+    "# rxn_smiles = \"[C:1]([C@@:2]([O:3][H:12])([C:4]([N:5]([C:6](=[O:7])[H:16])[H:15])([H:13])[H:14])[H:11])([H:8])([H:9])[H:10]>>[C:1]([C@@:2]([O:3][H:12])([C:4](/[N:5]=[C:6](/[O:7][H:15])[H:16])([H:13])[H:14])[H:11])([H:8])([H:9])[H:10]\"\n",
+    "\n",
+    "# rxn_smiles = '[C:1](=[C:2]([C:3]([C:4]([C:5](=[C:6]([C:7]([H:17])[H:18])[H:16])[H:15])([H:13])[H:14])([H:11])[H:12])[H:10])([H:8])[H:9]>>[C:1]([C:2]1([H:10])[C:3]([H:11])([H:12])[C:4]([H:13])([H:14])[C:5]([H:15])=[C:6]([H:16])[C:7]1([H:17])[H:18])([H:8])[H:9]'\n",
+    "# rxn_smiles = '[C:1](=[C:2]([C:3]([C:4]([C:5]([C:6](=[C:7]([H:17])[H:18])[H:16])[H:15])([H:13])[H:14])([H:11])[H:12])[H:10])([H:8])[H:9]>>[C:1]1([H:8])([H:9])[C:2]([H:10])[C:3]([H:11])([H:12])[C:4]([H:13])([H:14])[C@@:5]1([C:6](=[C:7]([H:17])[H:18])[H:16])[H:15]'\n",
+    "# rxn_smiles = '[N:1]([c:2]1[c:3]([H:9])[n:4]([H:10])[c:5]([H:11])[n:6]1)([H:7])[H:8]>>[N:1]([C:2]1=[N:6][C:5]([H:11])=[N:4][C:3]1([H:9])[H:10])([H:7])[H:8]'\n",
+    "# rxn_smiles = \"[C:1]([C:2]([C:3]([H:11])([H:12])[H:13])([C:4]([C:5]#[N:6])([H:14])[H:15])[H:10])([H:7])([H:8])[H:9]>>[C:1]([C:2]([C:3]([H:11])([H:12])[H:13])([C:4]([N+:6]#[C-:5])([H:14])[H:15])[H:10])([H:7])([H:8])[H:9]\"\n",
+    "# rxn_smiles = \"[C:1]([C:2]([C:3]([H:11])([H:12])[H:13])([C:4]([C:5]#[N:6])([H:14])[H:15])[H:10])([H:7])([H:8])[H:9]>>[C:1]([C:2]([C:3]([H:11])([H:12])[H:13])=[C:4]([C:5]#[N:6])[H:14])([H:7])([H:8])[H:9].[H:10][H:15]\"\n",
+    "# rxn_smiles = '[C:1]([C:2]1([H:10])[C:3]([H:11])([H:12])[C:4]1([C:5]#[N:6])[H:13])([H:7])([H:8])[H:9]>>[C:1](=[C:2]([C:3]([C:4]1([H:13])[C:5]([H:8])=[N:6]1)([H:11])[H:12])[H:10])([H:7])[H:9]'\n",
+    "# rxn_smiles = '[C:1]([O:2][C:3]([C:4]([O:5][H:13])([H:11])[H:12])([H:9])[H:10])([H:6])([H:7])[H:8]>>[C:1]([O:2][C:3](=[C:4]([H:11])[H:12])[H:10])([H:6])([H:7])[H:8].[O:5]([H:9])[H:13]'\n",
+    "# rxn_smiles = \"[C:1]([C:2]1=[C:3]([H:13])[C:4]2([C:5]([H:14])([H:15])[H:16])[C:6]([H:17])=[C:7]([H:18])[C:8]1([H:19])[C:9]2([H:20])[H:21])([H:10])([H:11])[H:12]>>[C:1]([C:2]#[C:3][H:13])([H:10])([H:11])[H:12].[C:4]1([C:5]([H:14])([H:15])[H:16])=[C:6]([H:17])[C:7]([H:18])=[C:8]([H:19])[C:9]1([H:20])[H:21]\"\n",
+    "\n",
+    "# ts egnn failures\n",
+    "# rxn_smiles = '[N:1]#[C:2][C@@:3]1([H:8])[C:4]([H:9])([H:10])[C@@:5]2([H:11])[C:6]([H:12])([H:13])[N:7]12>>[N:1]#[C:2][C@@:3]1([H:8])[C:4]([H:9])([H:10])[C:5]([H:11])=[N+:7]1[C-:6]([H:12])[H:13]'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version='1.0' encoding='iso-8859-1'?>\n",
+       "<svg version='1.1' baseProfile='full'\n",
+       "              xmlns='http://www.w3.org/2000/svg'\n",
+       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
+       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
+       "                  xml:space='preserve'\n",
+       "width='800px' height='300px' viewBox='0 0 800 300'>\n",
+       "<!-- END OF HEADER -->\n",
+       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='800.0' height='300.0' x='0.0' y='0.0'> </rect>\n",
+       "<path class='bond-0 atom-0 atom-1' d='M 205.5,164.2 L 220.9,169.8 L 219.5,173.6 L 204.1,168.0 L 202.5,165.3 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-1 atom-0 atom-2' d='M 202.5,165.3 L 204.1,168.0 L 201.3,184.1 L 197.3,183.4 L 200.2,167.3 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-2 atom-0 atom-3' d='M 201.4,163.2 L 202.5,165.3 L 200.2,167.3 L 186.1,167.3 L 183.7,165.3 L 184.9,163.2 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-15 atom-0 atom-13' d='M 212.0,149.0 L 212.5,152.0 L 205.5,164.2 L 202.5,165.3 L 201.4,163.2 L 209.6,149.0 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-3 atom-3 atom-4' d='M 183.7,165.3 L 186.1,167.3 L 189.0,183.4 L 185.0,184.1 L 182.2,168.0 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-4 atom-3 atom-5' d='M 180.8,164.2 L 183.7,165.3 L 182.2,168.0 L 166.7,173.6 L 165.3,169.8 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-5 atom-3 atom-6' d='M 174.3,149.0 L 176.6,149.0 L 184.9,163.2 L 183.7,165.3 L 180.8,164.2 L 173.8,152.0 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-6 atom-6 atom-7' d='M 171.2,149.0 L 174.3,149.0 L 173.8,152.0 L 161.2,162.6 L 158.6,159.5 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-7 atom-6 atom-8' d='M 161.2,135.3 L 173.8,145.9 L 174.3,149.0 L 171.2,149.0 L 158.6,138.4 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-8 atom-6 atom-9' d='M 183.7,132.6 L 184.9,134.7 L 176.6,149.0 L 174.3,149.0 L 173.8,145.9 L 181.4,132.6 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-9 atom-9 atom-10' d='M 176.0,115.3 L 184.9,130.6 L 183.7,132.6 L 181.4,132.6 L 172.6,117.3 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-10 atom-9 atom-11' d='M 201.4,130.6 L 202.5,132.6 L 201.4,134.7 L 184.9,134.7 L 183.7,132.6 L 184.9,130.6 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-11 atom-11 atom-12' d='M 210.2,115.3 L 213.7,117.3 L 204.9,132.6 L 202.5,132.6 L 201.4,130.6 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-12 atom-11 atom-13' d='M 204.9,132.6 L 212.5,145.9 L 212.0,149.0 L 209.6,149.0 L 201.4,134.7 L 202.5,132.6 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-13 atom-13 atom-14' d='M 225.1,135.3 L 227.7,138.4 L 215.1,149.0 L 212.0,149.0 L 212.5,145.9 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<path class='bond-14 atom-13 atom-15' d='M 215.1,149.0 L 227.7,159.5 L 225.1,162.6 L 212.5,152.0 L 212.0,149.0 Z' style='fill:#FFFFAA;fill-rule:evenodd;fill-opacity:1;stroke:#FFFFAA;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />\n",
+       "<ellipse cx='202.5' cy='165.3' rx='3.8' ry='3.8' class='atom-0'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='220.2' cy='171.7' rx='4.4' ry='5.6' class='atom-1'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='199.3' cy='183.8' rx='4.4' ry='5.6' class='atom-2'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='183.7' cy='165.3' rx='3.8' ry='3.8' class='atom-3'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='187.0' cy='183.8' rx='4.4' ry='5.6' class='atom-4'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='166.0' cy='171.7' rx='4.4' ry='5.6' class='atom-5'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='174.3' cy='149.0' rx='3.8' ry='3.8' class='atom-6'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='159.9' cy='161.1' rx='4.4' ry='5.6' class='atom-7'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='159.9' cy='136.9' rx='4.4' ry='5.6' class='atom-8'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='183.7' cy='132.6' rx='3.8' ry='3.8' class='atom-9'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='174.3' cy='116.3' rx='4.4' ry='5.6' class='atom-10'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='202.5' cy='132.6' rx='3.8' ry='3.8' class='atom-11'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='212.0' cy='116.3' rx='4.4' ry='5.6' class='atom-12'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='212.0' cy='149.0' rx='3.8' ry='3.8' class='atom-13'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='226.4' cy='136.9' rx='4.4' ry='5.6' class='atom-14'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<ellipse cx='226.4' cy='161.1' rx='4.4' ry='5.6' class='atom-15'  style='fill:#FFFFAA;fill-rule:evenodd;stroke:#FFFFAA;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-0 atom-0 atom-1' d='M 202.5,165.3 L 216.5,170.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-1 atom-0 atom-2' d='M 202.5,165.3 L 200.1,179.2' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-2 atom-0 atom-3' d='M 202.5,165.3 L 183.7,165.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-3 atom-3 atom-4' d='M 183.7,165.3 L 186.2,179.2' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-4 atom-3 atom-5' d='M 183.7,165.3 L 169.7,170.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-5 atom-3 atom-6' d='M 183.7,165.3 L 174.3,149.0' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-6 atom-6 atom-7' d='M 174.3,149.0 L 163.6,157.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-7 atom-6 atom-8' d='M 174.3,149.0 L 163.6,140.0' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-8 atom-6 atom-9' d='M 174.3,149.0 L 183.7,132.6' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-9 atom-9 atom-10' d='M 183.7,132.6 L 176.9,120.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-10 atom-9 atom-11' d='M 183.7,132.6 L 202.5,132.6' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-10 atom-9 atom-11' d='M 185.3,135.5 L 200.9,135.5' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-11 atom-11 atom-12' d='M 202.5,132.6 L 209.3,120.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-12 atom-11 atom-13' d='M 202.5,132.6 L 212.0,149.0' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-13 atom-13 atom-14' d='M 212.0,149.0 L 222.7,140.0' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-14 atom-13 atom-15' d='M 212.0,149.0 L 222.7,157.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='bond-15 atom-13 atom-0' d='M 212.0,149.0 L 202.5,165.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path class='atom-1' d='M 216.9 167.4\n",
+       "L 218.1 167.4\n",
+       "L 218.1 171.1\n",
+       "L 222.4 171.1\n",
+       "L 222.4 167.4\n",
+       "L 223.6 167.4\n",
+       "L 223.6 175.9\n",
+       "L 222.4 175.9\n",
+       "L 222.4 172.0\n",
+       "L 218.1 172.0\n",
+       "L 218.1 175.9\n",
+       "L 216.9 175.9\n",
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+       "' fill='#000000'/>\n",
+       "<path class='note' d='M 632.0 114.0\n",
+       "L 633.8 114.0\n",
+       "L 633.8 108.2\n",
+       "L 631.8 108.7\n",
+       "L 631.6 108.1\n",
+       "L 634.0 107.0\n",
+       "L 634.8 107.1\n",
+       "L 634.8 114.0\n",
+       "L 636.3 114.0\n",
+       "L 636.3 114.9\n",
+       "L 632.0 114.9\n",
+       "L 632.0 114.0\n",
+       "' fill='#000000'/>\n",
+       "<path d='M 393.0,150.0 L 427.0,150.0' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 427.0,150.0 L 422.0,152.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 427.0,150.0 L 422.0,147.1' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<rdmc.reaction.Reaction at 0x7f7617dc90d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rxn = Reaction.from_reaction_smiles(rxn_smiles)\n",
+    "rxn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ts_ml.utils import trained_model_dir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Can't instantiate abstract class GaussianOptimizer with abstract method is_available",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mGaussianOptimizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Can't instantiate abstract class GaussianOptimizer with abstract method is_available"
+     ]
+    }
+   ],
+   "source": [
+    "GaussianOptimizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Can't instantiate abstract class GaussianOptimizer with abstract method is_available",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 13\u001b[0m\n\u001b[1;32m      2\u001b[0m embedder \u001b[38;5;241m=\u001b[39m TSEGNNGuesser(trained_model_dir, track_stats\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# trained_model_dir = \"../../ts_egnn/trained_models/ts_gcn/2022_03_18/\"\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# embedder = TSGCNGuesser(trained_model_dir, track_stats=True)\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# embedder = RMSDPPGuesser(track_stats=True)\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# embedder = AutoNEBGuesser(track_stats=True)\u001b[39;00m\n\u001b[1;32m     10\u001b[0m ts_gen \u001b[38;5;241m=\u001b[39m TSConformerGenerator(\n\u001b[1;32m     11\u001b[0m     rxn_smiles\u001b[38;5;241m=\u001b[39mrxn_smiles,\n\u001b[1;32m     12\u001b[0m     embedder\u001b[38;5;241m=\u001b[39membedder,\n\u001b[0;32m---> 13\u001b[0m     optimizer\u001b[38;5;241m=\u001b[39m\u001b[43mGaussianOptimizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m     14\u001b[0m     pruner\u001b[38;5;241m=\u001b[39mTorsionPruner(),\n\u001b[1;32m     15\u001b[0m     verifiers\u001b[38;5;241m=\u001b[39m[XTBFrequencyVerifier(track_stats\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)],\n\u001b[1;32m     16\u001b[0m     save_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./test_ts\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     17\u001b[0m     multiplicity\u001b[38;5;241m=\u001b[39mrxn\u001b[38;5;241m.\u001b[39mreactant_complex\u001b[38;5;241m.\u001b[39mGetSpinMultiplicity(),\n\u001b[1;32m     18\u001b[0m \n\u001b[1;32m     19\u001b[0m )\n",
+      "\u001b[0;31mTypeError\u001b[0m: Can't instantiate abstract class GaussianOptimizer with abstract method is_available"
+     ]
+    }
+   ],
+   "source": [
+    "trained_model_dir = trained_model_dir / \"ts_egnn/2022_07_10_am1\"\n",
+    "embedder = TSEGNNGuesser(trained_model_dir, track_stats=True)\n",
+    "\n",
+    "# trained_model_dir = \"../../ts_egnn/trained_models/ts_gcn/2022_03_18/\"\n",
+    "# embedder = TSGCNGuesser(trained_model_dir, track_stats=True)\n",
+    "\n",
+    "# embedder = RMSDPPGuesser(track_stats=True)\n",
+    "# embedder = AutoNEBGuesser(track_stats=True)\n",
+    "\n",
+    "ts_gen = TSConformerGenerator(\n",
+    "    rxn_smiles=rxn_smiles,\n",
+    "    embedder=embedder,\n",
+    "    optimizer=GaussianOptimizer(),\n",
+    "    pruner=TorsionPruner(),\n",
+    "    verifiers=[XTBFrequencyVerifier(track_stats=True)],\n",
+    "    save_dir=\"./test_ts\",\n",
+    "    multiplicity=rxn.reactant_complex.GetSpinMultiplicity(),\n",
+    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022/04/25 06:24:44 PM | TSConformerGenerator | INFO: Embedding stable species conformers...\n",
+      "2022/04/25 06:24:44 PM | StochasticConformerGenerator | INFO: Config specified: using default settings for loose config\n",
+      "2022/04/25 06:24:44 PM | StochasticConformerGenerator | INFO: Generating conformers for [C:1]([C:2]([C:3]([H:20])([H:21])[H:22])([O:4])[C:5]([H:23])([H:24])[H:25])([H:17])([H:18])[H:19]\n",
+      "2022/04/25 06:24:44 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 1: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:44 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
+      "2022/04/25 06:24:45 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
+      "2022/04/25 06:24:45 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 15 unique conformers\n",
+      "2022/04/25 06:24:45 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 2: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:45 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 20 unique conformers\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 3: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 3: optimizing initial guesses...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 3: pruning conformers...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 3: kept 23 unique conformers\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 4: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:46 PM | StochasticConformerGenerator | INFO: Iteration 4: optimizing initial guesses...\n",
+      "2022/04/25 06:24:47 PM | StochasticConformerGenerator | INFO: Iteration 4: pruning conformers...\n",
+      "2022/04/25 06:24:47 PM | StochasticConformerGenerator | INFO: Iteration 4: kept 25 unique conformers\n",
+      "2022/04/25 06:24:47 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 5: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:47 PM | StochasticConformerGenerator | INFO: Iteration 5: optimizing initial guesses...\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 5: pruning conformers...\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 5: kept 27 unique conformers\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 6: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 6: optimizing initial guesses...\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 6: pruning conformers...\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 6: kept 27 unique conformers\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Iteration 6: stop crietria reached\n",
+      "\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Config specified: using default settings for loose config\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: Generating conformers for [C:6]([C:7]([C:8]([H:29])([H:30])[H:31])([C:9]([H:32])([H:33])[H:34])[c:10]1[c:11]([H:35])[c:12]([H:36])[c:13]([O:14][H:37])[c:15]([H:38])[c:16]1[H:39])([H:26])([H:27])[H:28]\n",
+      "2022/04/25 06:24:48 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 1: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:49 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
+      "2022/04/25 06:24:49 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
+      "2022/04/25 06:24:49 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 19 unique conformers\n",
+      "2022/04/25 06:24:49 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 2: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:49 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
+      "2022/04/25 06:24:50 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
+      "2022/04/25 06:24:50 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 36 unique conformers\n",
+      "2022/04/25 06:24:50 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 3: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:50 PM | StochasticConformerGenerator | INFO: Iteration 3: optimizing initial guesses...\n",
+      "2022/04/25 06:24:51 PM | StochasticConformerGenerator | INFO: Iteration 3: pruning conformers...\n",
+      "2022/04/25 06:24:51 PM | StochasticConformerGenerator | INFO: Iteration 3: kept 49 unique conformers\n",
+      "2022/04/25 06:24:51 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 4: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:51 PM | StochasticConformerGenerator | INFO: Iteration 4: optimizing initial guesses...\n",
+      "2022/04/25 06:24:52 PM | StochasticConformerGenerator | INFO: Iteration 4: pruning conformers...\n",
+      "2022/04/25 06:24:52 PM | StochasticConformerGenerator | INFO: Iteration 4: kept 60 unique conformers\n",
+      "2022/04/25 06:24:52 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 5: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:52 PM | StochasticConformerGenerator | INFO: Iteration 5: optimizing initial guesses...\n",
+      "2022/04/25 06:24:53 PM | StochasticConformerGenerator | INFO: Iteration 5: pruning conformers...\n",
+      "2022/04/25 06:24:53 PM | StochasticConformerGenerator | INFO: Iteration 5: kept 68 unique conformers\n",
+      "2022/04/25 06:24:53 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 6: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:53 PM | StochasticConformerGenerator | INFO: Iteration 6: optimizing initial guesses...\n",
+      "2022/04/25 06:24:54 PM | StochasticConformerGenerator | INFO: Iteration 6: pruning conformers...\n",
+      "2022/04/25 06:24:54 PM | StochasticConformerGenerator | INFO: Iteration 6: kept 77 unique conformers\n",
+      "2022/04/25 06:24:54 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 7: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:54 PM | StochasticConformerGenerator | INFO: Iteration 7: optimizing initial guesses...\n",
+      "2022/04/25 06:24:55 PM | StochasticConformerGenerator | INFO: Iteration 7: pruning conformers...\n",
+      "2022/04/25 06:24:55 PM | StochasticConformerGenerator | INFO: Iteration 7: kept 86 unique conformers\n",
+      "2022/04/25 06:24:55 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 8: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:55 PM | StochasticConformerGenerator | INFO: Iteration 8: optimizing initial guesses...\n",
+      "2022/04/25 06:24:56 PM | StochasticConformerGenerator | INFO: Iteration 8: pruning conformers...\n",
+      "2022/04/25 06:24:56 PM | StochasticConformerGenerator | INFO: Iteration 8: kept 93 unique conformers\n",
+      "2022/04/25 06:24:56 PM | StochasticConformerGenerator | INFO: Iteration 8: stop crietria reached\n",
+      "\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Config specified: using default settings for loose config\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Generating conformers for [C:1]([C:2]([C:3]([H:20])([H:21])[H:22])([O:4][H:37])[C:5]([H:23])([H:24])[H:25])([H:17])([H:18])[H:19]\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 1: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 19 unique conformers\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 2: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:58 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
+      "2022/04/25 06:24:59 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
+      "2022/04/25 06:24:59 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 33 unique conformers\n",
+      "2022/04/25 06:24:59 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 3: embedding 20 initial guesses...\n",
+      "2022/04/25 06:24:59 PM | StochasticConformerGenerator | INFO: Iteration 3: optimizing initial guesses...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 3: pruning conformers...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 3: kept 45 unique conformers\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 4: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 4: optimizing initial guesses...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 4: pruning conformers...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 4: kept 51 unique conformers\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 5: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:00 PM | StochasticConformerGenerator | INFO: Iteration 5: optimizing initial guesses...\n",
+      "2022/04/25 06:25:01 PM | StochasticConformerGenerator | INFO: Iteration 5: pruning conformers...\n",
+      "2022/04/25 06:25:01 PM | StochasticConformerGenerator | INFO: Iteration 5: kept 57 unique conformers\n",
+      "2022/04/25 06:25:01 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 6: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:01 PM | StochasticConformerGenerator | INFO: Iteration 6: optimizing initial guesses...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 6: pruning conformers...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 6: kept 64 unique conformers\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 7: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 7: optimizing initial guesses...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 7: pruning conformers...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 7: kept 67 unique conformers\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 7: stop crietria reached\n",
+      "\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Config specified: using default settings for loose config\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Generating conformers for [C:6]([C:7]([C:8]([H:29])([H:30])[H:31])([C:9]([H:32])([H:33])[H:34])[c:10]1[c:11]([H:35])[c:12]([H:36])[c:13]([O:14])[c:15]([H:38])[c:16]1[H:39])([H:26])([H:27])[H:28]\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 1: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:02 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
+      "2022/04/25 06:25:03 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
+      "2022/04/25 06:25:03 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 18 unique conformers\n",
+      "2022/04/25 06:25:03 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 2: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:03 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
+      "2022/04/25 06:25:04 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
+      "2022/04/25 06:25:04 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 27 unique conformers\n",
+      "2022/04/25 06:25:04 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 3: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:04 PM | StochasticConformerGenerator | INFO: Iteration 3: optimizing initial guesses...\n",
+      "2022/04/25 06:25:05 PM | StochasticConformerGenerator | INFO: Iteration 3: pruning conformers...\n",
+      "2022/04/25 06:25:05 PM | StochasticConformerGenerator | INFO: Iteration 3: kept 37 unique conformers\n",
+      "2022/04/25 06:25:05 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 4: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:05 PM | StochasticConformerGenerator | INFO: Iteration 4: optimizing initial guesses...\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: Iteration 4: pruning conformers...\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: Iteration 4: kept 44 unique conformers\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 5: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: Iteration 5: optimizing initial guesses...\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: Iteration 5: pruning conformers...\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: Iteration 5: kept 51 unique conformers\n",
+      "2022/04/25 06:25:06 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 6: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:07 PM | StochasticConformerGenerator | INFO: Iteration 6: optimizing initial guesses...\n",
+      "2022/04/25 06:25:07 PM | StochasticConformerGenerator | INFO: Iteration 6: pruning conformers...\n",
+      "2022/04/25 06:25:07 PM | StochasticConformerGenerator | INFO: Iteration 6: kept 55 unique conformers\n",
+      "2022/04/25 06:25:07 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 7: embedding 20 initial guesses...\n",
+      "2022/04/25 06:25:07 PM | StochasticConformerGenerator | INFO: Iteration 7: optimizing initial guesses...\n",
+      "2022/04/25 06:25:08 PM | StochasticConformerGenerator | INFO: Iteration 7: pruning conformers...\n",
+      "2022/04/25 06:25:08 PM | StochasticConformerGenerator | INFO: Iteration 7: kept 59 unique conformers\n",
+      "2022/04/25 06:25:08 PM | StochasticConformerGenerator | INFO: Iteration 7: stop crietria reached\n",
+      "\n",
+      "2022/04/25 06:25:11 PM | TSConformerGenerator | INFO: Generating initial TS guesses...\n",
+      "2022/04/25 06:25:15 PM | TSConformerGenerator | INFO: Optimizing TS guesses...\n",
+      "2022/04/25 06:29:13 PM | TSConformerGenerator | INFO: Pruning TS guesses...\n",
+      "2022/04/25 06:29:13 PM | TSConformerGenerator | INFO: Pruned 0 TS conformers\n",
+      "2022/04/25 06:29:13 PM | TSConformerGenerator | INFO: Verifying TS guesses...\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt_ts_mol = ts_gen(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a97d83374c3e485391d3d4b3913b791a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='confId', max=4), Output()), _dom_classes=('widget-intera…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function rdmc.view.interactive_conformer_viewer.<locals>.<lambda>(confId)>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initial_guess_mol = RDKitMol.FromFile(\"./test_ts/ts_initial_guess_confs.sdf\", sameMol=True)\n",
+    "# initial_guess_mol = RDKitMol.FromFile(\"/home/lagnajit/test3/ts_initial_guess_confs.sdf\", sameMol=True)\n",
+    "interactive_conformer_viewer(initial_guess_mol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cdbe7d829504c07880a2d3f8d0cdfbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='confId', max=4), Output()), _dom_classes=('widget-intera…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function rdmc.view.interactive_conformer_viewer.<locals>.<lambda>(confId)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt_ts_mol = RDKitMol.FromFile(\"./test_ts/ts_optimized_confs.sdf\", sameMol=True)\n",
+    "# opt_ts_mol = RDKitMol.FromFile(\"/home/lagnajit/test3/ts_optimized_confs.sdf\", sameMol=True)\n",
+    "interactive_conformer_viewer(opt_ts_mol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[True, True, True, True, False, True, True, False, False, False]\n",
+      "[True, True, True, False, False, True, True, False, False, False]\n",
+      "{0: True, 1: True, 2: True, 3: False, 4: False, 5: True, 6: True, 7: False, 8: False, 9: False, 10: True, 11: False, 12: False, 13: True, 14: True, 15: False, 16: True, 17: False, 18: False, 19: False}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "test_dir = \"/home/lagnajit/test3/\"\n",
+    "freq_path = os.path.join(test_dir, \"freq_check_ids.pkl\")\n",
+    "irc_path = os.path.join(test_dir, \"irc_check_ids.pkl\")\n",
+    "workflow_path = os.path.join(test_dir, \"workflow_check_ids.pkl\")\n",
+    "\n",
+    "with open(freq_path, \"rb\") as f:\n",
+    "    freq_check = pickle.load(f)\n",
+    "with open(irc_path, \"rb\") as f:\n",
+    "    irc_check = pickle.load(f)\n",
+    "with open(workflow_path, \"rb\") as f:\n",
+    "    workflow_check = pickle.load(f)\n",
+    "\n",
+    "print(freq_check)\n",
+    "print(irc_check)\n",
+    "print(workflow_check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_ts_mol.KeepIDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keep_ids = {i: True for i in range(opt_ts_mol.GetNumConformers())}\n",
+    "energies = {i: i for i in range(opt_ts_mol.GetNumConformers())}\n",
+    "opt_ts_mol.KeepIDs = keep_ids\n",
+    "opt_ts_mol.Energies = energies\n",
+    "# setattr(opt_ts_mol._mol, \"KeepIDs\", keep_ids)\n",
+    "\n",
+    "opt_ts_mol.KeepIDs[0] = False\n",
+    "opt_ts_mol.KeepIDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copied_mol = opt_ts_mol.Copy(quickCopy=True, copy_attrs=[\"KeepIDs\", \"Energies\"])\n",
+    "copied_mol.KeepIDs\n",
+    "copied_mol.Energies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copied_mol.KeepIDs[1] = False\n",
+    "opt_ts_mol.KeepIDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdmc.conformer_generation.utils import mol_to_dict, dict_to_mol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mol_dict = mol_to_dict(opt_ts_mol, conf_copy_attrs=[\"KeepIDs\", \"Energies\"])\n",
+    "mol_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d2m_mol = dict_to_mol(mol_dict, conf_copy_attrs=[\"KeepIDs\", \"Energies\"])\n",
+    "d2m_mol.Energies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_ts_mol.KeepIDs\n",
+    "unique_ids = [0, 1, 2, 3, 4, 6]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"/home/lagnajit/Downloads/0/0/freq_check_ids.pkl\", \"rb\") as f:\n",
+    "    freq_check = pickle.load(f)\n",
+    "    \n",
+    "freq_check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"./test_ts/freq_check_ids.pkl\", \"rb\") as f:\n",
+    "    freq_check = pickle.load(f)\n",
+    "    \n",
+    "print(freq_check)\n",
+    "\n",
+    "\n",
+    "with open(\"./test_ts/workflow_check_ids.pkl\", \"rb\") as f:\n",
+    "    full_check = pickle.load(f)\n",
+    "    \n",
+    "print(full_check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(opt_ts_mol.GetNumConformers()):\n",
+    "    save_dir = os.path.join(\"./test_ts\", f\"sella_opt{i}\")\n",
+    "    props = run_xtb_calc(opt_ts_mol, confId=i, job=\"--hess\", save_dir=save_dir)\n",
+    "    print(props[\"frequencies\"][:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_ts_mol = RDKitMol.FromFile(\"../../../../Windows/Papers/ts2/scheme_examples/ts_initial_guess_confs_fig.sdf\", sameMol=True)\n",
+    "interactive_conformer_viewer(opt_ts_mol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check some examples\n",
+    "save_dir = \"../rdmc/conformer_exps/ts_conf_gen/exps/val_rxns_egnn/1000/\"\n",
+    "r_file = os.path.join(save_dir, \"reactant_confs.sdf\")\n",
+    "p_file = os.path.join(save_dir, \"product_confs.sdf\")\n",
+    "ts_guess_file = os.path.join(save_dir, \"ts_initial_guess_confs.sdf\")\n",
+    "ts_opt_file = os.path.join(save_dir, \"ts_optimized_confs.sdf\")\n",
+    "freq_ver_file = os.path.join(save_dir, \"freq_check_ids.pkl\")\n",
+    "irc_ver_file = os.path.join(save_dir, \"irc_check_ids.pkl\")\n",
+    "\n",
+    "with open(freq_ver_file, \"rb\") as f:\n",
+    "    freq_ids = pickle.load(f)\n",
+    "print(freq_ids)\n",
+    "\n",
+    "with open(irc_ver_file, \"rb\") as f:\n",
+    "    irc_ids = pickle.load(f)\n",
+    "print(irc_ids)\n",
+    "\n",
+    "guess_ts = RDKitMol.FromFile(ts_guess_file, sanitize=False, sameMol=True)\n",
+    "opt_ts = RDKitMol.FromFile(ts_opt_file, sanitize=False, sameMol=True)\n",
+    "\n",
+    "interactive_conformer_viewer(guess_ts)\n",
+    "interactive_conformer_viewer(opt_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make things look pretty with pymol\n",
+    "\n",
+    "# distance d1, o1, h1\n",
+    "# hide labels, d1\n",
+    "\n",
+    "# unbond c2, o1\n",
+    "# distance d2, c2, o1\n",
+    "# hide labels, d2\n",
+    "\n",
+    "# unbond h1, c1\n",
+    "# distance d3, h1, c1\n",
+    "# hide labels, d3\n",
+    "\n",
+    "# set dash_color, white\n",
+    "# set dash_gap, 0.15\n",
+    "# set dash_radius, 0.03"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# allow old models with pyg 1.7 to be compatible with new pyg 2.0\n",
+    "# layernorm was incorrectly implemented, so we need to change the shape\n",
+    "# of any \"node_norm\" weights/biases from [1] to [hidden_dim]\n",
+    "# previously, this meant that weights/biases in layernorm were incorrectly broadcasted\n",
+    "\n",
+    "# import torch\n",
+    "# from copy import deepcopy\n",
+    "\n",
+    "# trained_model_dir = \"../../ts_egnn/trained_models/ts_egnn/2022_02_07\"\n",
+    "# old_model = torch.load(os.path.join(trained_model_dir, \"best_model.ckpt\"))\n",
+    "\n",
+    "# new_state_dict = deepcopy(old_model[\"state_dict\"])\n",
+    "# for param in new_state_dict:\n",
+    "#     if \"node_norm\" in param:\n",
+    "#         new_param = new_state_dict[param] * torch.ones([64])\n",
+    "#         new_state_dict[param] = new_param\n",
+    "        \n",
+    "# old_model[\"state_dict\"] = new_state_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ts_ml1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ipython/TS Conformer Generation[WIP].ipynb b/ipython/notebooks_to_clean/TS Conformer Generation[WIP].ipynb
similarity index 100%
rename from ipython/TS Conformer Generation[WIP].ipynb
rename to ipython/notebooks_to_clean/TS Conformer Generation[WIP].ipynb
diff --git a/ipython/TS-GCN+RDMC_v1.ipynb b/ipython/notebooks_to_clean/TS-GCN+RDMC_v1.ipynb
similarity index 100%
rename from ipython/TS-GCN+RDMC_v1.ipynb
rename to ipython/notebooks_to_clean/TS-GCN+RDMC_v1.ipynb
diff --git a/ipython/TS-GCN+RDMC_v2.ipynb b/ipython/notebooks_to_clean/TS-GCN+RDMC_v2.ipynb
similarity index 100%
rename from ipython/TS-GCN+RDMC_v2.ipynb
rename to ipython/notebooks_to_clean/TS-GCN+RDMC_v2.ipynb
diff --git a/ipython/TS-GCN+RDMC_v3.ipynb b/ipython/notebooks_to_clean/TS-GCN+RDMC_v3.ipynb
similarity index 100%
rename from ipython/TS-GCN+RDMC_v3.ipynb
rename to ipython/notebooks_to_clean/TS-GCN+RDMC_v3.ipynb
diff --git a/ipython/TS-GCN+RDMC_v4.ipynb b/ipython/notebooks_to_clean/TS-GCN+RDMC_v4.ipynb
similarity index 100%
rename from ipython/TS-GCN+RDMC_v4.ipynb
rename to ipython/notebooks_to_clean/TS-GCN+RDMC_v4.ipynb
diff --git a/ipython/TS-GCN+RDMC_v5.ipynb b/ipython/notebooks_to_clean/TS-GCN+RDMC_v5.ipynb
similarity index 100%
rename from ipython/TS-GCN+RDMC_v5.ipynb
rename to ipython/notebooks_to_clean/TS-GCN+RDMC_v5.ipynb
diff --git a/ipython/Well Conformer Generation[WIP].ipynb b/ipython/notebooks_to_clean/Well Conformer Generation[WIP].ipynb
similarity index 100%
rename from ipython/Well Conformer Generation[WIP].ipynb
rename to ipython/notebooks_to_clean/Well Conformer Generation[WIP].ipynb
diff --git a/ipython/develop_ts_screener.ipynb b/ipython/notebooks_to_clean/develop_ts_screener.ipynb
similarity index 100%
rename from ipython/develop_ts_screener.ipynb
rename to ipython/notebooks_to_clean/develop_ts_screener.ipynb
diff --git a/ipython/ts_generation_pipeline_LP.ipynb b/ipython/notebooks_to_clean/ts_generation_pipeline_LP.ipynb
similarity index 100%
rename from ipython/ts_generation_pipeline_LP.ipynb
rename to ipython/notebooks_to_clean/ts_generation_pipeline_LP.ipynb
diff --git a/rdmc/conformer_generation/embedders/__init__.py b/rdmc/conformer_generation/embedders/__init__.py
index e5499d67..e5a7c2e0 100644
--- a/rdmc/conformer_generation/embedders/__init__.py
+++ b/rdmc/conformer_generation/embedders/__init__.py
@@ -1,3 +1,6 @@
 from rdmc.conformer_generation.embedders.etkdg import ETKDGEmbedder
 from rdmc.conformer_generation.embedders.geomol import GeoMolEmbedder
 from rdmc.conformer_generation.embedders.random import RandomEmbedder
+from rdmc.conformer_generation.embedders.torsional_diffusion import (
+    TorsionalDiffusionEmbedder,
+)