Converted MACE to use PythonForce (#114)

* Fixed errors in documentation

* Converted MACE to use PythonForce

* More test cases

Author: peastman

doc/userguide.md

@@ -81,6 +81,7 @@ are supported.
 | `mace-off23-small`<br>`mace-off23-medium`<br>`mace-off23-large`<br>`mace-off24-medium` | Pretrained [MACE-OFF](https://pubs.acs.org/doi/10.1021/jacs.4c07099) models |
 | `mace-mpa-0-medium` | Pretrained [MACE-MPA-0](https://github.com/ACEsuit/mace-foundations) model |
 | `mace-omat-0-small`<br>`mace-omat-0-medium` | Pretrained [MACE-OMAT-0](https://github.com/ACEsuit/mace-foundations) models |
+| `mace-omol-0-extra-large` | Pretrained MACE-OMOL-0 model |
 | `mace` | Custom MACE models specified with the `modelPath` argument |
 
 When creating MACE models, the following keyword arguments to the `MLPotential` constructor are supported.
@@ -95,6 +96,9 @@ When using MACE models, the following extra keyword arguments to `createSystem()
 | --- | --- |
 | `precision` | The numerical precision of the model. Supported options are `'single'` and `'double'`.  If `None`, the default precision of the model is used. |
 | `returnEnergyType` | Whether to return the interaction energy or the energy including the self-energy.  The default is `'interaction_energy'`. Supported options are `'interaction_energy'` and `'energy'`. |
+| `device` | The PyTorch device to perform calculations on, either a `torch.device` object or a string (such as `'cuda'` or `'cpu'`.)  If omitted, a device is chosen automatically. |
+| `charge` | The total charge of the system.  If omitted, it is assumed to be 0.  This is only used by MACE-OMOL-0.  For other models it is ignored. |
+| `multiplicity` | The spin multiplicity of the system.  If omitted, it is assumed to be 1.  This is only used by MACE-OMOL-0.  For other models it is ignored.  |
 
 ### AIMNet2
 

openmmml/mlpotential.py

@@ -115,6 +115,20 @@ def addForces(self,
         """
         raise NotImplementedError('Subclasses must implement addForces()')
 
+    def _getTorchDevice(self, args):
+        """This is a utility routine for use by subclasses that are implemented with PyTorch.  It selects what device
+        to use, which can either by specified by the user with the 'device' argument, or chosen automatically based on
+        the available hardware."""
+        import torch
+        if 'device' in args:
+            device = args['device']
+            if isinstance(device, str):
+                device = torch.device(device)
+            return device
+        if torch.cuda.is_available():
+            return torch.device('cuda')
+        return torch.device('cpu')
+
 
 class MLPotential(object):
     """A potential function that can be used in simulations.