Merge branch 'master' into pyaimnet2

# Conflicts:
#	.github/workflows/CI.yml
#	qcengine/programs/base.py
#	qcengine/programs/tests/test_programs.py
#	qcengine/testing.py
#	qcengine/tests/test_harness_canonical.py

.github/workflows/CI.yml

@@ -98,6 +98,12 @@ jobs:
             runs-on: windows-latest
             pytest: "-k 'not (hes2 or qchem)'"
 
+          - conda-env: mace
+            python-version: "3.10"
+            label: MACE
+            runs-on: ubuntu-latest
+            pytest: ""
+
           - conda-env: aimnet2
             python-version: 3.11
             label: AIMNET2

devtools/conda-envs/adcc.yaml

@@ -6,7 +6,6 @@ channels:
 dependencies:
   - adcc>=0.15.7
   - psi4>=1.8.1
-  - conda-forge/label/libint_dev::libint
 
   # Core
   - python

devtools/conda-envs/mace.yaml

@@ -0,0 +1,19 @@
+name: mace
+channels:
+  - conda-forge
+dependencies:
+    # Core
+  - python
+  - pyyaml
+  - py-cpuinfo
+  - psutil
+  - qcelemental >=0.12.0
+  - pydantic>=1.0.0
+
+  # mace deps
+  - pymace
+
+    # Testing
+  - pytest
+  - pytest-cov
+  - codecov

devtools/conda-envs/opt-disp-cf.yaml

@@ -1,6 +1,5 @@
 name: test
 channels:
-  - conda-forge/label/libint_dev
   - conda-forge
   - nodefaults
 dependencies:

devtools/conda-envs/opt-disp.yaml

@@ -1,10 +1,10 @@
 name: test
 channels:
-  - psi4/label/dev
   - conda-forge
   - defaults
+  - psi4/label/dev  # for old dftd3 and mp2d
 dependencies:
-  - psi4
+  - psi4=1.9.1
   - blas=*=mkl  # not needed but an example of disuading solver from openblas and old psi
   #- intel-openmp!=2019.5
   - rdkit
@@ -25,7 +25,6 @@ dependencies:
   - py-cpuinfo
   - psutil
   - qcelemental >=0.26.0
-  - pydantic>=1.0.0
   - msgpack-python
 
     # Testing

qcengine/programs/base.py

@@ -28,6 +28,7 @@
 from .torchani import TorchANIHarness
 from .turbomole import TurbomoleHarness
 from .xtb import XTBHarness
+from .mace import MACEHarness
 from .aimnet2 import AIMNET2Harness
 
 __all__ = ["register_program", "get_program", "list_all_programs", "list_available_programs"]
@@ -126,6 +127,7 @@ def list_available_programs() -> Set[str]:
 
 # AI
 register_program(TorchANIHarness())
+register_program(MACEHarness())
 register_program(AIMNET2Harness())
 
 # Molecular Mechanics

qcengine/programs/mace.py

@@ -0,0 +1,139 @@
+from typing import TYPE_CHECKING, Dict, Union
+from qcelemental.models import AtomicResult, Provenance, FailedOperation
+from qcelemental.util import safe_version, which_import
+from qcengine.exceptions import InputError
+from qcengine.programs.model import ProgramHarness
+from qcengine.units import ureg
+
+if TYPE_CHECKING:
+    from qcelemental.models import AtomicInput, FailedOperation
+    from qcengine.config import TaskConfig
+
+
+class MACEHarness(ProgramHarness):
+    """Can be used to execute a published MACE-OFF23 model or local mace model.
+    For more info on the MACE-OFF23 models see <https://doi.org/10.48550/arXiv.2312.15211>.
+    The models can be found at <https://github.com/ACEsuit/mace-off>
+    """
+
+    _CACHE = {}
+
+    _defaults = {
+        "name": "MACE",
+        "scratch": False,
+        "thread_safe": True,
+        "thread_parallel": False,
+        "node_parallel": False,
+        "managed_memory": False,
+    }
+    version_cache: Dict[str, str] = {}
+
+    def found(self, raise_error: bool = False) -> bool:
+        return which_import(
+            "mace",
+            return_bool=True,
+            raise_error=raise_error,
+            raise_msg="Please install via `mamba install pymace -c conda-forge`",
+        )
+
+    def get_version(self) -> str:
+        self.found(raise_error=True)
+
+        which_prog = which_import("mace")
+        if which_prog not in self.version_cache:
+            import mace
+
+            self.version_cache[which_prog] = safe_version(mace.__version__)
+
+        return self.version_cache[which_prog]
+
+    def load_model(self, name: str):
+        """Compile and cache the model to make it faster when calling many times in serial"""
+        model_name = name.lower()
+        if model_name in self._CACHE:
+            return self._CACHE[model_name]
+
+        import torch
+        from e3nn.util import jit
+
+        if model_name in ["small", "medium", "large"]:
+            from mace.calculators.foundations_models import mace_off
+
+            model = mace_off(model=model_name, return_raw_model=True)
+        else:
+            try:
+                model = torch.load(name, map_location=torch.device("cpu"))
+            except FileNotFoundError:
+                raise InputError(
+                    "The mace harness can only run local models or a MACE-OFF23 model (`small`, `medium`, `large`)"
+                )
+        comp_mod = jit.compile(model)
+        self._CACHE[model_name] = (comp_mod, float(model.r_max), model.atomic_numbers)
+        return self._CACHE[model_name]
+
+    def compute(self, input_data: "AtomicInput", config: "TaskConfig") -> Union["AtomicResult", "FailedOperation"]:
+
+        self.found(raise_error=True)
+
+        import mace
+        import numpy as np
+        import torch
+        from mace.data import AtomicData
+        from mace.data.utils import AtomicNumberTable, Configuration
+        from mace.tools.torch_geometric import DataLoader
+
+        torch.set_default_dtype(torch.float64)
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # Failure flag
+        ret_data = {"success": False}
+
+        # Build model
+        method = input_data.model.method
+
+        # load the torch model which can be a MACE-OFF23 or local model
+        model, r_max, atomic_numbers = self.load_model(name=method)
+
+        z_table = AtomicNumberTable([int(z) for z in atomic_numbers])
+        atomic_numbers = input_data.molecule.atomic_numbers
+        pbc = (False, False, False)
+        # set the cell as None and mace will automatically create a cell big enough to include all atoms
+        cell = None
+
+        config = Configuration(
+            atomic_numbers=atomic_numbers,
+            positions=input_data.molecule.geometry * ureg.conversion_factor("bohr", "angstrom"),
+            pbc=pbc,
+            cell=cell,
+        )
+
+        data_loader = DataLoader(
+            dataset=[AtomicData.from_config(config, z_table=z_table, cutoff=r_max)],
+            batch_size=1,
+            shuffle=False,
+            drop_last=False,
+        )
+        input_dict = next(iter(data_loader)).to_dict()
+        model.to(device)
+        mace_data = model(input_dict, compute_force=True)
+        ret_data["properties"] = {"return_energy": mace_data["energy"] * ureg.conversion_factor("eV", "hartree")}
+
+        if input_data.driver == "energy":
+            ret_data["return_result"] = ret_data["properties"]["return_energy"]
+        elif input_data.driver == "gradient":
+            ret_data["return_result"] = (
+                np.asarray(-1.0 * mace_data["forces"] * ureg.conversion_factor("eV / angstrom", "hartree / bohr"))
+                .ravel()
+                .tolist()
+            )
+
+        else:
+            raise InputError("MACE only supports the energy and gradient driver methods.")
+
+        ret_data["extras"] = input_data.extras.copy()
+        ret_data["provenance"] = Provenance(creator="mace", version=mace.__version__, routine="mace")
+        ret_data["schema_name"] = "qcschema_output"
+        ret_data["success"] = True
+
+        # Form up a dict first, then sent to BaseModel to avoid repeat kwargs which don't override each other
+        return AtomicResult(**{**input_data.dict(), **ret_data})

qcengine/programs/tests/test_programs.py

@@ -94,7 +94,6 @@ def test_psi4_wavefunction_task():
 
 @using("psi4")
 def test_psi4_internal_failure():
-
     mol = Molecule.from_data(
         """0 3
      O    0.000000000000     0.000000000000    -0.068516245955
@@ -206,7 +205,6 @@ def test_mopac_task():
 
 
 def test_random_failure_no_retries(failure_engine):
-
     failure_engine.iter_modes = ["input_error"]
     ret = qcng.compute(failure_engine.get_job(), failure_engine.name, raise_error=False)
     assert ret.error.error_type == "input_error"
@@ -219,7 +217,6 @@ def test_random_failure_no_retries(failure_engine):
 
 
 def test_random_failure_with_retries(failure_engine):
-
     failure_engine.iter_modes = ["random_error", "random_error", "random_error"]
     ret = qcng.compute(failure_engine.get_job(), failure_engine.name, raise_error=False, task_config={"retries": 2})
     assert ret.input_data["provenance"]["retries"] == 2
@@ -232,7 +229,6 @@ def test_random_failure_with_retries(failure_engine):
 
 
 def test_random_failure_with_success(failure_engine):
-
     failure_engine.iter_modes = ["random_error", "pass"]
     failure_engine.ncalls = 0
     ret = qcng.compute(failure_engine.get_job(), failure_engine.name, raise_error=False, task_config={"retries": 1})
@@ -385,6 +381,40 @@ def test_openmm_gaff_keywords(gaff_settings):
         assert ret.return_result == pytest.approx(expected_result, rel=1e-6)
 
 
+@using("mace")
+def test_mace_energy():
+    """
+    Test calculating the energy with mace
+    """
+    water = qcng.get_molecule("water")
+    atomic_input = AtomicInput(molecule=water, model={"method": "small", "basis": None}, driver="energy")
+
+    result = qcng.compute(atomic_input, "mace")
+    assert result.success
+    assert pytest.approx(result.return_result) == -76.47683956098838
+
+
+@using("mace")
+def test_mace_gradient():
+    """
+    Test calculating the gradient with mace
+    """
+    water = qcng.get_molecule("water")
+    expected_result = np.array(
+        [
+            [0.0, -2.1590400539385646e-18, -0.04178551770271103],
+            [0.0, -0.029712483642769006, 0.020892758851355515],
+            [0.0, 0.029712483642769006, 0.020892758851355518],
+        ]
+    )
+
+    atomic_input = AtomicInput(molecule=water, model={"method": "small", "basis": None}, driver="gradient")
+
+    result = qcng.compute(atomic_input, "mace")
+    assert result.success
+    assert pytest.approx(result.return_result) == expected_result
+
+
 @using("aimnet2")
 @pytest.mark.parametrize(
     "model, expected_energy",

qcengine/programs/tests/test_standard_suite.py

@@ -58,7 +58,7 @@ def clsd_open_pmols():
 _q1 = (qcng.exceptions.InputError, "unknown SCFTYPE", "no ROHF reference for NWChem hand-coded MP2.")
 _q2 = (qcng.exceptions.InputError, "CCTYP IS PROGRAMMED ONLY FOR SCFTYP=RHF OR ROHF", "no UHF CC in GAMESS.")
 _q3 = (qcng.exceptions.InputError, "ccsd: nopen is not zero", "no non-RHF reference for NWChem hand-coded CC.")
-_q6 = (qcng.exceptions.InputError, r"Only RHF/UHF(/RKS|) Hessians are currently implemented.", "no ROHF Hessian for Psi4 HF.")
+_q6 = (qcng.exceptions.InputError, r"Only RHF/UHF(/RKS|/RKS/UKS|) Hessians are currently implemented.", "no ROHF Hessian for Psi4 HF.")
 _q45 = (qcng.exceptions.UnknownError, "non-Abelian symmetry not permitted", "temporary excuse of failure. I think NWChem has fixed upstream.")
 
 _w1 = ("MP2 CORRELATION ENERGY", "nonstandard answer: NWChem TCE MP2 doesn't report singles (affects ROHF)")

qcengine/testing.py

@@ -185,6 +185,7 @@ def get_job(self):
     "turbomole": which("define", return_bool=True),
     "xtb": which_import("xtb", return_bool=True),
     "mrchem": is_program_new_enough("mrchem", "1.0.0"),
+    "mace": is_program_new_enough("mace", "0.3.2"),
     "aimnet2": which_import("pyaimnet2", return_bool=True),
 }
 _programs["openmm"] = _programs["rdkit"] and which_import(".openmm", package="simtk", return_bool=True)

qcengine/tests/test_harness_canonical.py

@@ -33,6 +33,7 @@
     ("cfour", {"method": "hf", "basis": "6-31G"}, {}),
     ("gamess", {"method": "hf", "basis": "n31"}, {"basis__NGAUSS": 6}),
     ("mctc-gcp", {"method": "dft/sv"}, {}),
+    ("mace", {"method": "small"}, {})
     ("aimnet2", {"method": "b973c"}, {})
     # add as programs available
     # ("terachem", {"method": "bad"}),
@@ -134,6 +135,7 @@ def test_compute_energy_qcsk_basis(program, model, keywords):
         ("gcp", {"method": "bad"}),
         ("mrchem", {"method": "bad"}),
         ("mctc-gcp", {"method": "bad"}),
+        ("mace", {"method": "bad"})
         # add as programs available
         # ("molpro", {"method": "bad"}),
         # ("terachem", {"method": "bad"}),

setup.py

@@ -46,7 +46,6 @@
             "Intended Audience :: Science/Research",
             "Programming Language :: Python :: 3 :: Only",
             "Programming Language :: Python :: 3",
-            "Programming Language :: Python :: 3.6",
             "Programming Language :: Python :: 3.7",
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",