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

Forcefield Name	MLFF	Reference	Description
CHGNet	CHGNet	10.1038/s42256-023-00716-3	Available via the chgnet and matgl packages
DeepMD	MLFF.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
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.
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