Support AIMNet2

openmmml/models/__init__.py

@@ -1 +1 @@
-from . import anipotential
+from . import anipotential, aimnet2potential

openmmml/models/aimnet2potential.py

@@ -0,0 +1,105 @@
+"""
+aimnet2potential.py: Implements the AIMNet2 potential function.
+
+This is part of the OpenMM molecular simulation toolkit originating from
+Simbios, the NIH National Center for Physics-Based Simulation of
+Biological Structures at Stanford, funded under the NIH Roadmap for
+Medical Research, grant U54 GM072970. See https://simtk.org.
+
+Portions copyright (c) 2021-2023 Stanford University and the Authors.
+Authors: Peter Eastman
+Contributors:
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the "Software"),
+to deal in the Software without restriction, including without limitation
+the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+THE AUTHORS, CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+USE OR OTHER DEALINGS IN THE SOFTWARE.
+"""
+
+from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory
+import openmm
+from openmm import unit
+from typing import Iterable, Optional, Union
+
+class AIMNet2PotentialImplFactory(MLPotentialImplFactory):
+    """This is the factory that creates AIMNet2PotentialImpl objects."""
+
+    def createImpl(self, name: str, **args) -> MLPotentialImpl:
+        return AIMNet2PotentialImpl(name)
+
+
+class AIMNet2PotentialImpl(MLPotentialImpl):
+    """This is the MLPotentialImpl implementing the AIMNet2 potential.
+    """
+
+    def __init__(self, name):
+        self.name = name
+
+    def addForces(self,
+                  topology: openmm.app.Topology,
+                  system: openmm.System,
+                  atoms: Optional[Iterable[int]],
+                  forceGroup: int,
+                  **args):
+        # Load the AIMNet2 model.
+
+        import torch
+        import openmmtorch
+        if self.name == 'aimnet2-wb97m-d3':
+            model = torch.load('aimnet2_wb97m-d3_ens.jpt')
+        else:
+            raise ValueError('Unsupported AIMNet2 model: '+self.name)
+
+        # Create the PyTorch model that will be invoked by OpenMM.
+
+        includedAtoms = list(topology.atoms())
+        if atoms is not None:
+            includedAtoms = [includedAtoms[i] for i in atoms]
+        numbers = torch.tensor([[atom.element.atomic_number for atom in includedAtoms]])
+        charge = torch.tensor([args['charge']], dtype=torch.float32)
+
+        class AIMNet2Force(torch.nn.Module):
+
+            def __init__(self, model, numbers, charge, atoms):
+                super(AIMNet2Force, self).__init__()
+                self.model = model
+                self.numbers = torch.nn.Parameter(numbers, requires_grad=False)
+                self.charge = torch.nn.Parameter(charge, requires_grad=False)
+                self.energyScale = (unit.ev/unit.item).conversion_factor_to(unit.kilojoules_per_mole)
+                if atoms is None:
+                    self.indices = None
+                else:
+                    self.indices = torch.tensor(sorted(atoms), dtype=torch.int64)
+
+            def forward(self, positions, boxvectors: Optional[torch.Tensor] = None):
+                positions = positions.to(torch.float32)
+                if self.indices is not None:
+                    positions = positions[self.indices]
+                args = {'coord': 10.0*positions.unsqueeze(0),
+                        'numbers': self.numbers,
+                        'charge': self.charge}
+                result = self.model(args)
+                return (self.energyScale*result['energy'], (10.0*self.energyScale)*result['forces'][0])
+
+        # Create the TorchForce and add it to the System.
+
+        module = torch.jit.script(AIMNet2Force(model, numbers, charge, atoms))
+        force = openmmtorch.TorchForce(module)
+        force.setForceGroup(forceGroup)
+        force.setOutputsForces(True)
+        system.addForce(force)
+
+MLPotential.registerImplFactory('aimnet2-wb97m-d3', AIMNet2PotentialImplFactory())