diff --git a/ipython/Conformer Generation Workflow.ipynb b/ipython/Conformer Generation Workflow.ipynb
new file mode 100644
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+++ b/ipython/Conformer Generation Workflow.ipynb	
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stable species conformer search\n",
+    "\n",
+    "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",
+    "- pruning (torsion fingerprints, CREGEN)\n",
+    "- convergence metrics (conformational entropy/partition function)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3a45eb9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdmc.conformer_generation.embedders import *\n",
+    "from rdmc.conformer_generation.optimizers import *\n",
+    "from rdmc.conformer_generation.pruners import *\n",
+    "from rdmc.conformer_generation.metrics import *\n",
+    "from rdmc.conformer_generation.generators import StochasticConformerGenerator\n",
+    "\n",
+    "from rdmc import RDKitMol\n",
+    "from rdmc.view import mol_viewer, interactive_conformer_viewer, conformer_viewer\n",
+    "\n",
+    "T = 298  # K\n",
+    "R = 0.0019872  # kcal/(K*mol)\n",
+    "HARTREE_TO_KCAL_MOL = 627.503\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0216632",
+   "metadata": {},
+   "source": [
+    "## 1. Test embedder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79826cc2",
+   "metadata": {},
+   "source": [
+    "Create the 3D geometry for the molecule specified by the SMILES (`smi`). Currently it has no 3D conformer embedded, therefore the visualization returns a 2D illustration of the molecule"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "61a6eb92",
+   "metadata": {},
+   "outputs": [
+    {
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+       "viewer_17104484122786984.zoomTo();\n",
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+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<py3Dmol.view at 0x7f146851f800>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b022193",
+   "metadata": {},
+   "source": [
+    "### 1.1 ETKDG embedder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "886ca5f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f8b9af06",
+   "metadata": {},
+   "outputs": [
+    {
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+       "viewer_1710448412406751.zoomTo();\n",
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+       "\tviewer_1710448412406751.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
+       "\tviewer_1710448412406751.addPropertyLabels(\"index\",\"\",{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
+       "\tviewer_1710448412406751.zoomTo();\n",
+       "viewer_1710448412406751.render();\n",
+       "});\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<py3Dmol.view at 0x7f1467185130>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize_conf_id = 2\n",
+    "\n",
+    "mol_viewer(mol, confId=visualize_conf_id)  # visualize the molecule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7424c20e",
+   "metadata": {},
+   "source": [
+    "### 1.2 GeoMol Conformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49022146",
+   "metadata": {},
+   "source": [
+    "Supported options:\n",
+    "- `dataset`: `drug` or `qm9`\n",
+    "- `device`: `cpu`, or `cuda` (or specific cuda device like `cuda:0`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9431fbc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_confs = 10  # Number of conformers to create\n",
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ac12a75",
+   "metadata": {},
+   "outputs": [
+    {
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+       "        </div>\n",
+       "<script>\n",
+       "\n",
+       "var loadScriptAsync = function(uri){\n",
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+       "\tviewer_171044841477305.setStyle({\"stick\": {\"radius\": 0.05, \"color\": \"#f2f2f2\"}, \"sphere\": {\"scale\": 0.25}});\n",
+       "\tviewer_171044841477305.addPropertyLabels(\"index\",\"\",{\"fontSize\": 15, \"fontColor\": \"white\", \"alignment\": \"center\", \"showBackground\": true, \"backgroundOpacity\": 0.2, \"backgroundColor\": \"black\"});\n",
+       "\tviewer_171044841477305.zoomTo();\n",
+       "viewer_171044841477305.render();\n",
+       "});\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<py3Dmol.view at 0x7f1466c129c0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize_conf_id = 2\n",
+    "\n",
+    "mol_viewer(mol, confId=visualize_conf_id)  # visualize the molecule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46be5c44",
+   "metadata": {},
+   "source": [
+    "## 2. Create a conformer generation workflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a35cd583",
+   "metadata": {},
+   "source": [
+    "### 2.1 Choose each components\n",
+    "- embedder\n",
+    "- optimizer\n",
+    "- pruner\n",
+    "- metric\n",
+    "\n",
+    "you can also use default config by providing `config` to the generator. You can open a new cell and use `StochasticConformerGenerator.set_config?` to check what is the default configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "92bfdbc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# embedder = ETKDGEmbedder(track_stats=True)\n",
+    "embedder = GeoMolEmbedder(dataset=\"drugs\", track_stats=True, temp_schedule=\"none\", device=\"cpu\") # Initialize conformer embedder\n",
+    "optimizer = XTBOptimizer()\n",
+    "pruner = TorsionPruner(max_chk_threshold=30)\n",
+    "metric = SCGMetric(metric=\"entropy\", window=5, threshold=0.005)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0522a7ff",
+   "metadata": {},
+   "source": [
+    "### 2.2 Conformer generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9f099b43",
+   "metadata": {},
+   "outputs": [
+    {
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+       "viewer_17104484148209493.render();\n",
+       "});\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<py3Dmol.view at 0x7f14662f9f40>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "smi = \"CN1C2=C(C=C(C=C2)Cl)C(=NCC1=O)C3=CC=CC=C3\"\n",
+    "\n",
+    "mol_viewer(RDKitMol.FromSmiles(smi))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e72b2651",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024/03/14 04:33:34 PM | StochasticConformerGenerator | INFO: Generating conformers for CN1C2=C(C=C(C=C2)Cl)C(=NCC1=O)C3=CC=CC=C3\n",
+      "2024/03/14 04:33:34 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 1: embedding 100 initial guesses...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024/03/14 04:33:35 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
+      "2024/03/14 04:33:39 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
+      "2024/03/14 04:33:40 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 12 unique conformers\n",
+      "2024/03/14 04:33:40 PM | StochasticConformerGenerator | INFO: \n",
+      "Iteration 2: embedding 100 initial guesses...\n",
+      "2024/03/14 04:33:40 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
+      "2024/03/14 04:33:45 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
+      "2024/03/14 04:33:45 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 12 unique conformers\n",
+      "2024/03/14 04:33:45 PM | StochasticConformerGenerator | INFO: Iteration 2: stop crietria reached\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of conformers: 12\n",
+      "Metric: 4.938e-03\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_conformers_per_iter = 100\n",
+    "min_iters = 2\n",
+    "max_iters = 5\n",
+    "\n",
+    "scg = StochasticConformerGenerator(\n",
+    "    smiles=smi,\n",
+    "    embedder=embedder,\n",
+    "    optimizer=optimizer,\n",
+    "    pruner=pruner,\n",
+    "    metric=metric,\n",
+    "    min_iters=min_iters,\n",
+    "    max_iters=max_iters,\n",
+    ")\n",
+    "\n",
+    "unique_mol_data = scg(n_conformers_per_iter)\n",
+    "print(\n",
+    "    f\"Number of conformers: {len(unique_mol_data)}\\n\"\n",
+    "    f\"Metric: {scg.metric.metric_history[-1]:.3e}\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "GeoMol",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipython/Generate Atommapped SMILES.ipynb b/ipython/Generate Atommapped SMILES.ipynb
index 83e9f39c..1572a373 100644
--- a/ipython/Generate Atommapped SMILES.ipynb	
+++ b/ipython/Generate Atommapped SMILES.ipynb	
@@ -838,7 +838,7 @@
    "source": [
     "new_rxn = Reaction(r_complex, p_complex)\n",
     "display(new_rxn)\n",
-    "print(rxn.to_smiles())"
+    "print(new_rxn.to_smiles())"
    ]
   },
   {
@@ -870,7 +870,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.9.18"
   },
   "vscode": {
    "interpreter": {
diff --git a/ipython/stochastic_conf_pipeline_LP.ipynb b/ipython/stochastic_conf_pipeline_LP.ipynb
deleted file mode 100644
index 3b1f782d..00000000
--- a/ipython/stochastic_conf_pipeline_LP.ipynb
+++ /dev/null
@@ -1,327 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stable species conformer search\n",
-    "Leverage ETKDG for stochastic conformer generation\n",
-    "\n",
-    "Use this as a base for ML 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",
-    "- pruning (torsion fingerprints, CREGEN)\n",
-    "- convergence metrics (conformational entropy/partition function)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rdmc.conformer_generation.embedders import *\n",
-    "from rdmc.conformer_generation.optimizers import *\n",
-    "from rdmc.conformer_generation.pruners import *\n",
-    "from rdmc.conformer_generation.metrics import *\n",
-    "from rdmc.conformer_generation.generators import StochasticConformerGenerator\n",
-    "\n",
-    "from rdmc.view import mol_viewer, interactive_conformer_viewer, conformer_viewer\n",
-    "\n",
-    "T = 298  # K\n",
-    "R = 0.0019872  # kcal/(K*mol)\n",
-    "HARTREE_TO_KCAL_MOL = 627.503"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rdkit import Chem\n",
-    "\n",
-    "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]\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022/04/01 03:17:42 PM | StochasticConformerGenerator | INFO: Config specified: using default settings for normal config\n",
-      "2022/04/01 03:17:42 PM | StochasticConformerGenerator | INFO: Generating conformers for [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]\n",
-      "2022/04/01 03:17:42 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 1: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:42 PM | StochasticConformerGenerator | INFO: Iteration 1: optimizing initial guesses...\n",
-      "2022/04/01 03:17:45 PM | StochasticConformerGenerator | INFO: Iteration 1: pruning conformers...\n",
-      "2022/04/01 03:17:45 PM | StochasticConformerGenerator | INFO: Iteration 1: kept 9 unique conformers\n",
-      "2022/04/01 03:17:45 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 2: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:46 PM | StochasticConformerGenerator | INFO: Iteration 2: optimizing initial guesses...\n",
-      "2022/04/01 03:17:49 PM | StochasticConformerGenerator | INFO: Iteration 2: pruning conformers...\n",
-      "2022/04/01 03:17:49 PM | StochasticConformerGenerator | INFO: Iteration 2: kept 18 unique conformers\n",
-      "2022/04/01 03:17:49 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 3: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:49 PM | StochasticConformerGenerator | INFO: Iteration 3: optimizing initial guesses...\n",
-      "2022/04/01 03:17:52 PM | StochasticConformerGenerator | INFO: Iteration 3: pruning conformers...\n",
-      "2022/04/01 03:17:52 PM | StochasticConformerGenerator | INFO: Iteration 3: kept 27 unique conformers\n",
-      "2022/04/01 03:17:52 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 4: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:52 PM | StochasticConformerGenerator | INFO: Iteration 4: optimizing initial guesses...\n",
-      "2022/04/01 03:17:55 PM | StochasticConformerGenerator | INFO: Iteration 4: pruning conformers...\n",
-      "2022/04/01 03:17:55 PM | StochasticConformerGenerator | INFO: Iteration 4: kept 35 unique conformers\n",
-      "2022/04/01 03:17:55 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 5: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:55 PM | StochasticConformerGenerator | INFO: Iteration 5: optimizing initial guesses...\n",
-      "2022/04/01 03:17:59 PM | StochasticConformerGenerator | INFO: Iteration 5: pruning conformers...\n",
-      "2022/04/01 03:17:59 PM | StochasticConformerGenerator | INFO: Iteration 5: kept 43 unique conformers\n",
-      "2022/04/01 03:17:59 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 6: embedding 100 initial guesses...\n",
-      "2022/04/01 03:17:59 PM | StochasticConformerGenerator | INFO: Iteration 6: optimizing initial guesses...\n",
-      "2022/04/01 03:18:02 PM | StochasticConformerGenerator | INFO: Iteration 6: pruning conformers...\n",
-      "2022/04/01 03:18:02 PM | StochasticConformerGenerator | INFO: Iteration 6: kept 44 unique conformers\n",
-      "2022/04/01 03:18:02 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 7: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:02 PM | StochasticConformerGenerator | INFO: Iteration 7: optimizing initial guesses...\n",
-      "2022/04/01 03:18:05 PM | StochasticConformerGenerator | INFO: Iteration 7: pruning conformers...\n",
-      "2022/04/01 03:18:05 PM | StochasticConformerGenerator | INFO: Iteration 7: kept 46 unique conformers\n",
-      "2022/04/01 03:18:05 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 8: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:05 PM | StochasticConformerGenerator | INFO: Iteration 8: optimizing initial guesses...\n",
-      "2022/04/01 03:18:08 PM | StochasticConformerGenerator | INFO: Iteration 8: pruning conformers...\n",
-      "2022/04/01 03:18:08 PM | StochasticConformerGenerator | INFO: Iteration 8: kept 52 unique conformers\n",
-      "2022/04/01 03:18:08 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 9: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:09 PM | StochasticConformerGenerator | INFO: Iteration 9: optimizing initial guesses...\n",
-      "2022/04/01 03:18:12 PM | StochasticConformerGenerator | INFO: Iteration 9: pruning conformers...\n",
-      "2022/04/01 03:18:12 PM | StochasticConformerGenerator | INFO: Iteration 9: kept 50 unique conformers\n",
-      "2022/04/01 03:18:12 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 10: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:12 PM | StochasticConformerGenerator | INFO: Iteration 10: optimizing initial guesses...\n",
-      "2022/04/01 03:18:15 PM | StochasticConformerGenerator | INFO: Iteration 10: pruning conformers...\n",
-      "2022/04/01 03:18:15 PM | StochasticConformerGenerator | INFO: Iteration 10: kept 49 unique conformers\n",
-      "2022/04/01 03:18:15 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 11: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:15 PM | StochasticConformerGenerator | INFO: Iteration 11: optimizing initial guesses...\n",
-      "2022/04/01 03:18:18 PM | StochasticConformerGenerator | INFO: Iteration 11: pruning conformers...\n",
-      "2022/04/01 03:18:18 PM | StochasticConformerGenerator | INFO: Iteration 11: kept 54 unique conformers\n",
-      "2022/04/01 03:18:18 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 12: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:18 PM | StochasticConformerGenerator | INFO: Iteration 12: optimizing initial guesses...\n",
-      "2022/04/01 03:18:21 PM | StochasticConformerGenerator | INFO: Iteration 12: pruning conformers...\n",
-      "2022/04/01 03:18:22 PM | StochasticConformerGenerator | INFO: Iteration 12: kept 52 unique conformers\n",
-      "2022/04/01 03:18:22 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 13: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:22 PM | StochasticConformerGenerator | INFO: Iteration 13: optimizing initial guesses...\n",
-      "2022/04/01 03:18:25 PM | StochasticConformerGenerator | INFO: Iteration 13: pruning conformers...\n",
-      "2022/04/01 03:18:25 PM | StochasticConformerGenerator | INFO: Iteration 13: kept 59 unique conformers\n",
-      "2022/04/01 03:18:25 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 14: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:25 PM | StochasticConformerGenerator | INFO: Iteration 14: optimizing initial guesses...\n",
-      "2022/04/01 03:18:28 PM | StochasticConformerGenerator | INFO: Iteration 14: pruning conformers...\n",
-      "2022/04/01 03:18:28 PM | StochasticConformerGenerator | INFO: Iteration 14: kept 66 unique conformers\n",
-      "2022/04/01 03:18:28 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 15: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:28 PM | StochasticConformerGenerator | INFO: Iteration 15: optimizing initial guesses...\n",
-      "2022/04/01 03:18:31 PM | StochasticConformerGenerator | INFO: Iteration 15: pruning conformers...\n",
-      "2022/04/01 03:18:31 PM | StochasticConformerGenerator | INFO: Iteration 15: kept 74 unique conformers\n",
-      "2022/04/01 03:18:31 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 16: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:32 PM | StochasticConformerGenerator | INFO: Iteration 16: optimizing initial guesses...\n",
-      "2022/04/01 03:18:35 PM | StochasticConformerGenerator | INFO: Iteration 16: pruning conformers...\n",
-      "2022/04/01 03:18:35 PM | StochasticConformerGenerator | INFO: Iteration 16: kept 76 unique conformers\n",
-      "2022/04/01 03:18:35 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 17: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:35 PM | StochasticConformerGenerator | INFO: Iteration 17: optimizing initial guesses...\n",
-      "2022/04/01 03:18:38 PM | StochasticConformerGenerator | INFO: Iteration 17: pruning conformers...\n",
-      "2022/04/01 03:18:38 PM | StochasticConformerGenerator | INFO: Iteration 17: kept 72 unique conformers\n",
-      "2022/04/01 03:18:38 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 18: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:38 PM | StochasticConformerGenerator | INFO: Iteration 18: optimizing initial guesses...\n",
-      "2022/04/01 03:18:41 PM | StochasticConformerGenerator | INFO: Iteration 18: pruning conformers...\n",
-      "2022/04/01 03:18:41 PM | StochasticConformerGenerator | INFO: Iteration 18: kept 68 unique conformers\n",
-      "2022/04/01 03:18:41 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 19: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:41 PM | StochasticConformerGenerator | INFO: Iteration 19: optimizing initial guesses...\n",
-      "2022/04/01 03:18:44 PM | StochasticConformerGenerator | INFO: Iteration 19: pruning conformers...\n",
-      "2022/04/01 03:18:44 PM | StochasticConformerGenerator | INFO: Iteration 19: kept 65 unique conformers\n",
-      "2022/04/01 03:18:44 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 20: embedding 100 initial guesses...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022/04/01 03:18:45 PM | StochasticConformerGenerator | INFO: Iteration 20: optimizing initial guesses...\n",
-      "2022/04/01 03:18:48 PM | StochasticConformerGenerator | INFO: Iteration 20: pruning conformers...\n",
-      "2022/04/01 03:18:48 PM | StochasticConformerGenerator | INFO: Iteration 20: kept 66 unique conformers\n",
-      "2022/04/01 03:18:48 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 21: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:48 PM | StochasticConformerGenerator | INFO: Iteration 21: optimizing initial guesses...\n",
-      "2022/04/01 03:18:51 PM | StochasticConformerGenerator | INFO: Iteration 21: pruning conformers...\n",
-      "2022/04/01 03:18:51 PM | StochasticConformerGenerator | INFO: Iteration 21: kept 65 unique conformers\n",
-      "2022/04/01 03:18:51 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 22: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:51 PM | StochasticConformerGenerator | INFO: Iteration 22: optimizing initial guesses...\n",
-      "2022/04/01 03:18:54 PM | StochasticConformerGenerator | INFO: Iteration 22: pruning conformers...\n",
-      "2022/04/01 03:18:54 PM | StochasticConformerGenerator | INFO: Iteration 22: kept 63 unique conformers\n",
-      "2022/04/01 03:18:54 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 23: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:54 PM | StochasticConformerGenerator | INFO: Iteration 23: optimizing initial guesses...\n",
-      "2022/04/01 03:18:58 PM | StochasticConformerGenerator | INFO: Iteration 23: pruning conformers...\n",
-      "2022/04/01 03:18:58 PM | StochasticConformerGenerator | INFO: Iteration 23: kept 63 unique conformers\n",
-      "2022/04/01 03:18:58 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 24: embedding 100 initial guesses...\n",
-      "2022/04/01 03:18:58 PM | StochasticConformerGenerator | INFO: Iteration 24: optimizing initial guesses...\n",
-      "2022/04/01 03:19:01 PM | StochasticConformerGenerator | INFO: Iteration 24: pruning conformers...\n",
-      "2022/04/01 03:19:01 PM | StochasticConformerGenerator | INFO: Iteration 24: kept 65 unique conformers\n",
-      "2022/04/01 03:19:01 PM | StochasticConformerGenerator | INFO: \n",
-      "Iteration 25: embedding 100 initial guesses...\n",
-      "2022/04/01 03:19:01 PM | StochasticConformerGenerator | INFO: Iteration 25: optimizing initial guesses...\n",
-      "2022/04/01 03:19:04 PM | StochasticConformerGenerator | INFO: Iteration 25: pruning conformers...\n",
-      "2022/04/01 03:19:04 PM | StochasticConformerGenerator | INFO: Iteration 25: kept 63 unique conformers\n",
-      "2022/04/01 03:19:04 PM | StochasticConformerGenerator | INFO: Iteration 25: stop crietria reached\n",
-      "\n",
-      "2022/04/01 03:19:04 PM | StochasticConformerGenerator | INFO: Calling CRESTPruner\n",
-      "2022/04/01 03:19:04 PM | StochasticConformerGenerator | INFO: Calling XTBOptimizer\n",
-      "2022/04/01 03:19:13 PM | StochasticConformerGenerator | INFO: Calling CRESTPruner\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9 0.008233205857910033\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedder = ETKDGEmbedder(track_stats=True)\n",
-    "# embedder = GeoMolEmbedder(\"../rdmc/external/GeoMol/trained_models/both/\", dataset=\"drugs\", track_stats=True, temp_schedule=\"none\")\n",
-    "optimizer = XTBOptimizer()\n",
-    "pruner = TorsionPruner(max_chk_threshold=30)\n",
-    "metric = SCGMetric(metric=\"entropy\", window=5, threshold=0.005)\n",
-    "n_conformers_per_iter = 100\n",
-    "\n",
-    "scg = StochasticConformerGenerator(\n",
-    "    smiles=smi,\n",
-    "    config=\"normal\",\n",
-    "    embedder=embedder,\n",
-    "    min_iters=5\n",
-    ")\n",
-    "\n",
-    "unique_mol_data = scg(n_conformers_per_iter)\n",
-    "print(len(unique_mol_data), scg.metric.metric_history[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a006a637bd1943c0b5db28b99366abe4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='confId', max=8), Output()), _dom_classes=('widget-intera…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function rdmc.view.interactive_conformer_viewer.<locals>.<lambda>(confId)>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mol = dict_to_mol(unique_mol_data)\n",
-    "interactive_conformer_viewer(mol)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_smiles = [\n",
-    "    \"C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O\",\n",
-    "    \"CC[C@@H]1CCCC[C@@H]1C\",\n",
-    "    \"CCCCCCCCCC\",\n",
-    "    \"CC1=CCCC(C1/C=C/C(=O)C)(C)C\",\n",
-    "    \"C[C@H]1CC[C@H](CC2=C1CC[C@@H]2C)C(C)(C)O\",\n",
-    "    \"N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc2ccccc2)C(O)=O\",\n",
-    "    \"C1COCC(=O)N1C2=CC=C(C=C2)N3C[C@@H](OC3=O)CNC(=O)C4=CC=C(S4)Cl\",\n",
-    "    \"CCCCCOC(=O)NC1=NC(=O)N(C=C1F)[C@H]2[C@@H]([C@@H]([C@H](O2)C)O)O\",\n",
-    "    \"CCC(CC)O[C@@H]1C=C(C[C@@H]([C@H]1NC(=O)C)N)C(=O)OCC\",\n",
-    "    \"C[C@]12CC[C@@H](CC1=CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC=C4C5=CN=CC=C5)C)O\",\n",
-    "    \"C1CCN(C1)CCOC2=C3COC/C=C/COCC4=CC(=CC=C4)C5=NC(=NC=C5)NC(=C3)C=C2\",\n",
-    "    \"C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O\",\n",
-    "    \"CC(C)C[C@@H](C1=C(C(=C(C(=C1O)C=O)O)C=O)O)[C@]2(CC[C@@H]3[C@@H]2[C@H]4[C@H](C4(C)C)CC[C@@]3(C)O)C\",\n",
-    "    \"C[C@H](N)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)O\",\n",
-    "    \"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C\",\n",
-    "    \"C[C@@H]1C[C@H]2CC[C@H](O2)[C@@H](C(=O)O[C@H](C[C@@H]3CC[C@@H](O3)[C@H](C(=O)O[C@@H](C[C@H]4CC[C@H](O4)[C@@H](C(=O)O[C@H](C[C@@H]5CC[C@@H](O5)[C@H](C(=O)O1)C)C)C)C)C)C)C\",\n",
-    "    \"CC[C@@]1(C[C@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O\",\n",
-    "    \"CC(C)[C@@H](C(=O)N1CC2(CC2)C[C@H]1C3=NC=C(N3)C4=CC5=C(C=C4)C6=C(C5(F)F)C=C(C=C6)C7=CC8=C(C=C7)N=C(N8)[C@@H]9[C@H]1CC[C@H](C1)N9C(=O)[C@H](C(C)C)NC(=O)OC)NC(=O)OC\",\n",
-    "    \"C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OCCO)C)/C)O)OC)C)C)/C)OC\",\n",
-    "    \"C[C@H]1[C@H]([C@@](C[C@@H](O1)O[C@@H]2[C@H]([C@@H]([C@H](O[C@H]2OC3=C4C=C5C=C3OC6=C(C=C(C=C6)[C@H]([C@H](C(=O)N[C@H](C(=O)N[C@H]5C(=O)N[C@@H]7C8=CC(=C(C=C8)O)C9=C(C=C(C=C9O)O)[C@H](NC(=O)[C@H]([C@@H](C1=CC(=C(O4)C=C1)Cl)O)NC7=O)C(=O)O)CC(=O)N)NC(=O)[C@@H](CC(C)C)NC)O)Cl)CO)O)O)(C)N)O\"\n",
-    "]\n",
-    "\n",
-    "import csv\n",
-    "\n",
-    "with open(\"./../rdmc/conformer_exps/organic_conf_gen/smiles.csv\", \"w\") as f:\n",
-    "    writer = csv.writer(f, delimiter=\"\\n\")\n",
-    "    writer.writerow(test_smiles)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:rdmc_env] *",
-   "language": "python",
-   "name": "conda-env-rdmc_env-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/rdmc/conformer_generation/embedders.py b/rdmc/conformer_generation/embedders.py
index 211eb3c2..ff9c548c 100644
--- a/rdmc/conformer_generation/embedders.py
+++ b/rdmc/conformer_generation/embedders.py
@@ -5,27 +5,27 @@
 Modules for providing initial guess geometries
 """
 
+from pathlib import Path
+
 from rdmc import RDKitMol
-import os.path as osp
-import yaml
+
 import numpy as np
 from time import time
-
-try:
-    import torch
-    from torch_geometric.data import Batch
-except ImportError:
-    pass
-
 from .utils import *
 
+# GeoMol relevant imports
 try:
     import torch
-    from rdmc.external.GeoMol.model.model import GeoMol
-    from rdmc.external.GeoMol.model.featurization import featurize_mol_from_smiles
-    from rdmc.external.GeoMol.model.inference import construct_conformers
-except ImportError:
+    from geomol.model import GeoMol
+    from geomol.featurization import featurize_mol_from_smiles, from_data_list
+    from geomol.inference import construct_conformers
+    from geomol.utils import model_path as geomol_model_path
+    import yaml  # only used to load GeoMol parameters
+except ImportError as e:
+    GeoMol = None
+    print(e)
     print("No GeoMol installation detected. Skipping import...")
+    print("Please install the GeoMol fork at https://github.com/xiaoruiDong/GeoMol")
 
 
 class ConfGenEmbedder:
@@ -58,8 +58,10 @@ def update_mol(self, smiles: str):
             # Copy the graph but remove conformers
             self.mol = self.mol.Copy(quickCopy=True)
 
-    def embed_conformers(self,
-                         n_conformers: int):
+    def embed_conformers(
+        self,
+        n_conformers: int
+    ):
         """
         Embed conformers according to the molecule graph.
 
@@ -71,10 +73,11 @@ def embed_conformers(self,
         """
         raise NotImplementedError
 
-    def update_stats(self,
-                     n_trials: int,
-                     time: float = 0.
-                     ) -> dict:
+    def update_stats(
+        self,
+        n_trials: int,
+        time: float = 0.
+    ) -> dict:
         """
         Update the statistics of the conformer generation.
 
@@ -88,10 +91,12 @@ def update_stats(self,
         n_success = self.mol.GetNumConformers()
         self.n_success = n_success
         self.percent_success = n_success / n_trials * 100
-        stats = {"iter": self.iter,
-                 "time": time,
-                 "n_success": self.n_success,
-                 "percent_success": self.percent_success}
+        stats = {
+            "iter": self.iter,
+            "time": time,
+            "n_success": self.n_success,
+            "percent_success": self.percent_success
+        }
         self.stats.append(stats)
         return stats
 
@@ -104,9 +109,11 @@ def write_mol_data(self):
         """
         return mol_to_dict(self.mol, copy=False, iter=self.iter)
 
-    def __call__(self,
-                 smiles: str,
-                 n_conformers: int):
+    def __call__(
+        self,
+        smiles: str,
+        n_conformers: int
+    ):
         """
         Embed conformers according to the molecule graph.
 
@@ -137,28 +144,36 @@ class GeoMolEmbedder(ConfGenEmbedder):
     Embed conformers using GeoMol.
 
     Args:
-            trained_model_dir (str): Directory of the trained model.
+            trained_model_dir (str, optional): Directory of the trained model. If not provided, the models distributed with the package will be used.
             dataset (str, optional): Dataset used for training. Defaults to ``"drugs"``.
             temp_schedule (str, optional): Temperature schedule. Defaults to ``"linear"``.
             track_stats (bool, optional): Whether to track the statistics of the conformer generation. Defaults to ``False``.
     """
 
-    def __init__(self,
-                 trained_model_dir: str,
-                 dataset: str = "drugs",
-                 temp_schedule: str = "linear",
-                 track_stats: bool = False):
+    def __init__(
+        self,
+        trained_model_dir: str = None,
+        dataset: str = "drugs",
+        temp_schedule: str = "linear",
+        track_stats: bool = False,
+        device: str = 'cpu',
+    ):
+        if GeoMol is None:
+            raise ImportError("No GeoMol installation detected. Please install the GeoMol fork at https://github.com/xiaoruiDong/GeoMol.")
         super(GeoMolEmbedder, self).__init__(track_stats)
 
         # TODO: add option of pre-pruning geometries using alpha values
-        # TODO: inverstigate option of changing "temperature" each iteration to sample diverse geometries
+        # TODO: investigate option of changing "temperature" each iteration to sample diverse geometries
+        self.device = device
 
-        with open(osp.join(trained_model_dir, "model_parameters.yml")) as f:
+        trained_model_dir = geomol_model_path / dataset if trained_model_dir is None else Path(trained_model_dir)
+        with open(trained_model_dir / "model_parameters.yml") as f:
             model_parameters = yaml.full_load(f)
         model = GeoMol(**model_parameters)
 
-        state_dict = torch.load(osp.join(trained_model_dir, "best_model.pt"), map_location=torch.device('cpu'))
+        state_dict = torch.load(trained_model_dir / "best_model.pt", map_location=torch.device(device))
         model.load_state_dict(state_dict, strict=True)
+        model.to(self.device)
         model.eval()
         self.model = model
         self.tg_data = None
@@ -166,8 +181,14 @@ def __init__(self,
         self.temp_schedule = temp_schedule
         self.dataset = dataset
 
-    def embed_conformers(self,
-                         n_conformers: int):
+    def to(self, device: str):
+        self.device = device
+        self.model.to(device)
+
+    def embed_conformers(
+        self,
+        n_conformers: int
+    ):
         """
         Embed conformers according to the molecule graph.
 
@@ -186,11 +207,11 @@ def embed_conformers(self,
         # featurize data and run GeoMol
         if self.tg_data is None:
             self.tg_data = featurize_mol_from_smiles(self.smiles, dataset=self.dataset)
-        data = Batch.from_data_list([self.tg_data])  # need to run this bc of dumb internal GeoMol processing
+        data = from_data_list([self.tg_data]).to(self.device)  # need to run this bc of dumb internal GeoMol processing
         self.model(data, inference=True, n_model_confs=n_conformers)
 
         # process predictions
-        model_coords = construct_conformers(data, self.model).double().cpu().detach().numpy()
+        model_coords = construct_conformers(data, self.model, self.device).double().cpu().detach().numpy()
         split_model_coords = np.split(model_coords, n_conformers, axis=1)
 
         # package in mol and return
diff --git a/rdmc/conformer_generation/optimizers.py b/rdmc/conformer_generation/optimizers.py
index 09118d14..e90cf744 100644
--- a/rdmc/conformer_generation/optimizers.py
+++ b/rdmc/conformer_generation/optimizers.py
@@ -192,7 +192,7 @@ def optimize_conformers(self,
             positions = opt_mol.GetPositions()
             conf = new_mol.GetConformer(id=c_id)
             conf.SetPositions(positions)
-            energy = float(opt_mol.GetProp('total energy / Eh'))  # * HARTREE_TO_KCAL_MOL  # kcal/mol (TODO: check)
+            energy = props['total energy']
             mol_data[c_id].update({"positions": positions,  # issues if not all opts succeeded?
                                    "conf": conf,
                                    "energy": energy})
diff --git a/rdmc/external/logparser/base.py b/rdmc/external/logparser/base.py
index 017593a7..a164be3d 100644
--- a/rdmc/external/logparser/base.py
+++ b/rdmc/external/logparser/base.py
@@ -19,7 +19,7 @@
 try:
     from ipywidgets import interact, IntSlider, Dropdown, FloatLogSlider
 except ImportError:
-    pass
+    interact = None
 
 
 class BaseLog(object):
@@ -270,6 +270,12 @@ def get_scf_energies(self,
             # sub2 stores the energies for subsequent jobs e.g., multiple sps
             if 'opt' in self.job_type or 'scan' in self.job_type:
                 sub1 = scf_energies[:num_opt_geoms][self.get_converged_geom_idx()]
+            elif 'irc' in self.job_type:
+                # If taking corrector steps and job failed due to corrector fails
+                # There is one more energy value compared to the number of geometries
+                sub1 = scf_energies[: len(self.cclib_results.optstatus)][
+                    self.get_converged_geom_idx()
+                ]
             else:
                 sub1 = scf_energies[self.get_converged_geom_idx()]
             if 'scan' not in self.job_type:
@@ -700,6 +706,9 @@ def interact_opt(self,
         Returns:
             interact
         """
+        if interact is None:
+            raise ImportError('interact is not installed. Please install it by `pip install ipywidgets`.')
+
         mol = self.get_mol(converged=False, sanitize=sanitize, backend=backend)
         xyzs = self.get_xyzs(converged=False)
         sdfs = [mol.ToMolBlock(confId=i) for i in range(mol.GetNumConformers())]
@@ -838,6 +847,9 @@ def view_freq(self,
         Returns:
             interact
         """
+        if interact is None:
+            raise ImportError('interact is not installed. Please install it by `pip install ipywidgets`.')
+
         xyz = self.get_xyzs(converged=True)[0]
         lines = xyz.splitlines()
         vib_xyz_list = lines[0:2]
@@ -851,6 +863,9 @@ def interact_freq(self):
         """
         Create a IPython interactive widget to investigate the frequency calculation.
         """
+        if interact is None:
+            raise ImportError('interact is not installed. Please install it by `pip install ipywidgets`.')
+
         dropdown = Dropdown(
             options=self.freqs,
             value=self.freqs[0],
@@ -1018,6 +1033,9 @@ def interact_irc(self,
         Returns:
             interact
         """
+        if interact is None:
+            raise ImportError('interact is not installed. Please install it by `pip install ipywidgets`.')
+
         mol = self._process_irc_mol(sanitize=sanitize, converged=converged, backend=backend, bothway=bothway)
         sdfs = [mol.ToMolBlock(confId=i) for i in range(mol.GetNumConformers())]
         xyzs = self.get_xyzs(converged=converged)
@@ -1188,6 +1206,9 @@ def interact_scan(self,
         Returns:
             interact
         """
+        if interact is None:
+            raise ImportError('interact is not installed. Please install it by `pip install ipywidgets`.')
+
         mol = self._process_scan_mol(align_scan=align_scan,
                                      align_frag_idx=align_frag_idx,
                                      sanitize=sanitize,
diff --git a/rdmc/external/logparser/gaussian.py b/rdmc/external/logparser/gaussian.py
index 04b639c9..082ccaa2 100644
--- a/rdmc/external/logparser/gaussian.py
+++ b/rdmc/external/logparser/gaussian.py
@@ -116,7 +116,7 @@ def _update_schemes(self):
         scheme_str = ''.join(line.strip('\n')[1:] for line in scheme_lines[1:])
 
         try:
-            self._schemes = scheme_to_dict(scheme_str)
+            self._schemes = scheme_to_dict(scheme_str.lower())
         except Exception as e:
             print(f'Calculation scheme parser encounters a problem. \nGot: {e}\n'
                   f'Feel free to raise an issue about this error at RDMC\'s Github Repo.')
diff --git a/rdmc/external/xtb_tools/opt.py b/rdmc/external/xtb_tools/opt.py
index e0235872..df02ae9c 100644
--- a/rdmc/external/xtb_tools/opt.py
+++ b/rdmc/external/xtb_tools/opt.py
@@ -7,7 +7,7 @@
 """
 
 import json
-import os
+from pathlib import Path
 from shutil import rmtree
 import subprocess
 import tempfile
@@ -25,7 +25,7 @@
     TS_PATH_INP,
 )
 
-XTB_INPUT_FILE = os.path.join(UTILS_PATH, "xtb.inp")
+XTB_INPUT_FILE = Path(UTILS_PATH) / "xtb.inp"
 
 
 def read_xtb_json(json_file, mol):
@@ -48,6 +48,7 @@ def read_xtb_json(json_file, mol):
     atoms = [ATOMNUM_TO_ELEM[atom.GetAtomicNum()] for atom in mol.GetAtoms()]
     atomic_energy = sum([ATOM_ENERGIES_XTB[atom] for atom in atoms])
     props = {
+        "total energy": data["total energy"],
         "E_form": data["total energy"] - atomic_energy,  # already in Hartree
         "E_homo": E_homo * EV_TO_HARTREE,
         "E_lumo": E_lumo * EV_TO_HARTREE,
@@ -130,19 +131,20 @@ def run_xtb_calc(mol, confId=0, job="", return_optmol=False, method="gfn2", leve
     method = "--" + method
     input_file = TS_PATH_INP if job == "--path" else ""
 
-    temp_dir = os.path.abspath(save_dir) if save_dir else tempfile.mkdtemp()
-    logfile = os.path.join(temp_dir, "xtb.log")
-    xtb_out = os.path.join(temp_dir, "xtbout.json")
-    xtb_wbo = os.path.join(temp_dir, "wbo")
-    xtb_g98 = os.path.join(temp_dir, "g98.out")
-    xtb_ts = os.path.join(temp_dir, "xtbpath_ts.xyz")
+    temp_dir = Path(save_dir).absolute() if save_dir else Path(tempfile.mkdtemp()).absolute()
+    logfile = temp_dir / "xtb.log"
+    xtb_out = temp_dir / "xtbout.json"
+    xtb_wbo = temp_dir / "wbo"
+    xtb_g98 = temp_dir / "g98.out"
+    xtb_ts = temp_dir / "xtbpath_ts.xyz"
 
-    sdf_path = os.path.join(temp_dir, "mol.sdf")
-    mol.ToSDFFile(sdf_path, confId=confId)
+    sdf_path = temp_dir / "mol.sdf"
+    mol.ToSDFFile(str(sdf_path), confId=confId)
+    update_rdkit_mol_format(sdf_path)
 
     command = [
         XTB_BINARY,
-        sdf_path,
+        str(sdf_path),
         xtb_command,
         method,
         level,
@@ -156,9 +158,10 @@ def run_xtb_calc(mol, confId=0, job="", return_optmol=False, method="gfn2", leve
     ]
 
     if job == "--path":
-        p_sdf_path = os.path.join(temp_dir, "pmol.sdf")
-        pmol.ToSDFFile(p_sdf_path, confId=pconfId)
-        command.insert(3, p_sdf_path)
+        p_sdf_path = temp_dir / "pmol.sdf"
+        pmol.ToSDFFile(str(p_sdf_path), confId=pconfId)
+        update_rdkit_mol_format(sdf_path)
+        command.insert(3, str(p_sdf_path))
 
     with open(logfile, "w") as f:
         xtb_run = subprocess.run(
@@ -183,7 +186,7 @@ def run_xtb_calc(mol, confId=0, job="", return_optmol=False, method="gfn2", leve
                         [line for line in log_data if "GEOMETRY OPTIMIZATION CONVERGED AFTER" in line][-1].split()[-3])
                 except IndexError:
                     # logfile doesn't exist for [H]
-                    if not os.path.exists(os.path.join(temp_dir, "xtbopt.sdf")):
+                    if not (temp_dir / "xtbopt.sdf").exists():
                         not save_dir and rmtree(temp_dir)
                         raise ValueError(f"xTB calculation failed.")
                     else:
@@ -200,7 +203,7 @@ def run_xtb_calc(mol, confId=0, job="", return_optmol=False, method="gfn2", leve
 
         if job == "--path":
             try:
-                opt_mol = RDKitMol.FromFile(os.path.join(temp_dir, "xtbpath_ts.xyz"))
+                opt_mol = RDKitMol.FromFile(str(temp_dir / "xtbpath_ts.xyz"))
             except FileNotFoundError:
                 return (props, None) if return_optmol else props
             # props.update(read_xtb_json(xtb_out, opt_mol))
@@ -208,13 +211,51 @@ def run_xtb_calc(mol, confId=0, job="", return_optmol=False, method="gfn2", leve
             return (props, opt_mol) if return_optmol else props
 
         if method == "--gff":
-            opt_mol = RDKitMol.FromFile(os.path.join(temp_dir, "xtbopt.sdf"))[0]
+            opt_mol = RDKitMol.FromFile(str(temp_dir / "xtbopt.sdf"))[0]
+            try:
+                with open(temp_dir / "gfnff_lists.json", "r") as f:
+                    props["total energy"] = json.load(f)["total energy"]
+            except FileNotFoundError:
+                props["total energy"] = 0.
             not save_dir and rmtree(temp_dir)
             return (props, opt_mol) if return_optmol else props
 
         props.update(read_xtb_json(xtb_out, mol))
         if return_optmol:
-            opt_mol = RDKitMol.FromFile(os.path.join(temp_dir, "xtbopt.sdf"))[0]
+            opt_mol = RDKitMol.FromFile(str(temp_dir / "xtbopt.sdf"))[0]
         props.update({"wbo": get_wbo(xtb_wbo)})
         not save_dir and rmtree(temp_dir)
         return (props, opt_mol) if return_optmol else props
+
+
+def update_rdkit_mol_format(path):
+    """
+    After xTB changes its parser backend to mctc-lib, it stops being able to read Mol/SDF
+    files generated from RDKit. This is due to, in the bond property section, mctc-lib
+    looks for 7 elements while RDKit only generates 4. As xTB doesn't really need to know
+    the extra information, we can simply assign them to 0s. This patch function helps
+    append the missing 0s.
+    """
+
+    with open(path, "r") as f:
+        lines = f.readlines()
+
+    n_atoms = int(lines[3].split()[0])
+    n_bonds = int(lines[3].split()[1])
+
+    # Check if the file needs to be fixed, only check once
+    n_bond_props = len(lines[4 + n_atoms].split())
+    if n_bond_props > 7:
+        raise ValueError("This SDF/Mol file is abnormal, please double check your file")
+    elif n_bond_props == 7:  # No need to fix
+        return
+    else:
+        n_0s = 7 - n_bond_props
+
+    new_lines = lines[:4 + n_atoms] + [
+        line[:-1] + "  0" * n_0s + "\n"
+        for line in lines[4 + n_atoms: 4 + n_atoms + n_bonds]
+    ] + lines[4 + n_atoms + n_bonds:]
+
+    with open(path, "w") as f:
+        f.writelines(new_lines)
diff --git a/rdmc/view.py b/rdmc/view.py
index 944bb2b0..63829e02 100644
--- a/rdmc/view.py
+++ b/rdmc/view.py
@@ -11,7 +11,10 @@
 
 from rdmc.mol import RDKitMol
 from rdmc.ts import clean_ts
-from ipywidgets import interact, IntSlider
+try:
+    from ipywidgets import interact, IntSlider
+except ImportError:
+    interact = None
 
 
 def mol_viewer(obj: str,
@@ -162,6 +165,9 @@ def interactive_conformer_viewer(mol, **kwargs):
     Returns:
         py3Dmol.view: The molecule viewer with slider to view different conformers.
     """
+    if not interact:
+        raise ImportError("This function requires ipywidgets to be installed. You can install it by pip or conda")
+
     if isinstance(mol, list) or isinstance(mol, tuple):
         def viewer(confId):
             return mol_viewer(obj=mol[confId], confId=0, **kwargs)