This is a high level API for using machine learning models in OpenMM simulations. With just a few lines of code, you can set up a simulation that uses a standard, pretrained model to represent some or all of the interactions in a system.
In the current release, the supported potential functions are ANI-1ccx and ANI-2x, using the implementations in TorchANI, as well as MACE models, including the pre-trained MACE-OFF23 models, utilizing the MACE implementation. They are suitable for small molecules involving a limited set of elements and no charges. Future releases will add new potential functions that support a much wider range of molecules.
You can find the current documentation at https://openmm.github.io/openmm-ml
OpenMM-ML can be installed with conda or mamba.
mamba install -c conda-forge openmm-mlWe recommend using mamba, since it is faster and less buggy than conda.
To use this package, create a MLPotential object, specifying the name of the potential function to use. You can then call createSystem() to create a System object for a simulation. For example,
from openmmml import MLPotential
potential = MLPotential('ani2x')
system = potential.createSystem(topology)Alternatively, you can use createMixedSystem() to create a System where part is modeled with this potential and the rest is modeled with a conventional force field. As an example, suppose the Topology contains three chains. Chain 0 is a protein, chain 1 is a ligand, and chain 2 is solvent. The following code creates a System in which the internal energy of the ligand is computed with ANI2x, while everything else (including interactions between the ligand and the rest of the System) is computed with Amber14.
forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')
mm_system = forcefield.createSystem(topology)
chains = list(topology.chains())
ml_atoms = [atom.index for atom in chains[1].atoms()]
potential = MLPotential('ani2x')
ml_system = potential.createMixedSystem(topology, mm_system, ml_atoms)