Machine Learning forcefields / interatomic potentials

atomate2 includes an interface to a few common machine learning interatomic potentials (MLIPs), also known variously as machine learning forcefields (MLFFs), or foundation potentials (FPs) for universal variants.

Most of Maker classes using the forcefields inherit from atomate2.forcefields.utils.ForceFieldMixin to specify which forcefield to use. The ForceFieldMixin mixin provides the following configurable parameters:

• force_field_name: Name of the forcefield to use.

• calculator_kwargs: Keyword arguments to pass to the corresponding ASE calculator.

These parameters are passed to atomate2.forcefields.utils.ase_calculator() to instantiate the appropriate ASE calculator.

The force_field_name should be either one of predefined atomate2.forcefields.utils.MLFF (or its string equivalent) or a dictionary decodable as a class or function for ASE calculator as follows.

Using predefined forcefields supported via atomate2.forcefields.utils.MLFF

Support is provided for the following models, which can be selected using atomate2.forcefields.utils.MLFF, as shown in the table below (in alphabetical order): You need only install packages for the forcefields you wish to use.

Forcefield Name	MLFF	Reference	Description
Allegro	Allegro	10.1038/s41467-023-36329-y	Requires the nequip-allegro package
CHGNet	CHGNet	10.1038/s42256-023-00716-3	Available via the chgnet and matgl packages
DeepMD	DeepMD	10.1103/PhysRevB.108.L180104	The Deep Potential model used for this test is UniPero, a universal interatomic potential for perovskite oxides. It can be downloaded here
FAIRChem	FAIRChem	Meta’s FAIRChem Github	Proprietary, requires extra authentication. See notes below.
Gaussian Approximation Potential (GAP)	GAP	10.1103/PhysRevLett.104.136403	Relies on quippy-ase package
M3GNet	M3GNet	10.1038/s43588-022-00349-3	Relies on matgl package
MACE-MP-0	MACE or MACE_MP_0 (recommended)	10.1063/5.0297006	Relies on mace_torch and optionally torch_dftd packages
MACE-MP-0b3	MACE_MP_0B3	10.1063/5.0297006	Relies on mace_torch and optionally torch_dftd packages
MACE-MPA-0	MACE_MPA_0	10.1063/5.0297006	Relies on mace_torch and optionally torch_dftd packages
MatPES-PBE	MATPES_PBE	10.48550/arXiv.2503.04070	Relies on matgl. Defaults to TensorNet architecture, but can also use M3GNet or CHGNet architectures via kwargs. See atomate2.forcefields.utils._DEFAULT_CALCULATOR_KWARGS for more options.
MatPES-r2SCAN	MATPES_R2SCAN	10.48550/arXiv.2503.04070	Relies on matgl. Defaults to TensorNet architecture, but can also use M3GNet or CHGNet architectures via kwargs. See atomate2.forcefields.utils._DEFAULT_CALCULATOR_KWARGS for more options.
MatterSim	MatterSim	arXiv:2405.04967	Requires the mattersim package
Neuroevolution Potential (NEP)	NEP	10.1103/PhysRevB.104.104309	Relies on calorine package
Neural Equivariant Interatomic Potentials (Nequip)	Nequip	10.1038/s41467-022-29939-5	Relies on the nequip package
SevenNet	SevenNet	10.1021/acs.jctc.4c00190	Relies on the sevenn package

Using custom forcefields by dictionary

force_field_name also accepts a MSONable dictionary for specifying a custom ASE calculator class or function [1]. For example, a Job created with the following code snippet instantiates chgnet.model.dynamics.CHGNetCalculator as the ASE calculator:

job = ForceFieldStaticMaker(
    force_field_name={
        "@module": "chgnet.model.dynamics",
        "@callable": "CHGNetCalculator",
    }
).make(structure)

Notes on FairChem (Meta) models {#fairchem-notes}

The FAIRChem models provided by Meta require extra authentication via HuggingFace:

1. Request access to the UMA models via HuggingFace. You will need to set up a HuggingFace account. You will need to receive approval for the UMA models to proceed.

2. Install the HuggingFace CLI with pip install 'huggingface_hub'.

3. Run huggingface-cli login from a shell to authenticate your session. You will need to set up an access token.

4. You can now use the FAIRChem calculators. The general syntax for setting up a FAIRChem calculator in atomate2 is:

calculator_kwargs = {
    "predict_unit": {"model_name": "uma-s-1p1"},
    "task_name": "omat",
}

atomate2 will then set up a FAIRChemCalculator:

from atomate2.forcefields.utils import MLFF, _DEFAULT_CALCULATOR_KWARGS
from fairchem.core import FAIRChemCalculator, pretrained_mlip

predict_unit_kwargs = calculator_kwargs.pop(
    "predict_unit", _DEFAULT_CALCULATOR_KWARGS[MLFF.FAIRChem]["predict_unit"]
)
calculator = FAIRChemCalculator(
    pretrained_mlip.get_predict_unit(predict_unit_kwargs),
    **{k: v for k, v in calculator_kwargs.items() if k != "predict_unit"},
)

The default in atomate2 is the OMat24 model with uma-s-1p1.

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[1]

In this context, the type annotation of the decoded dict should be either Type[Calculator] or Callable[..., Calculator], where Calculator is from ase.calculators.calculator.