Force Fields Python Packages

chgnet

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

nequip

NequIP is a code for building E(3)-equivariant interatomic potentials

xnns

Neural network interatomic potentials for chemistry and materials science

moltemplate

A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies

graph-pes

train and use graph-based ML models of potential energy surfaces

nequip-allegro

Allegro is a code for building highly scalable E(3)-equivariant interatomic potentials

openff-toolkit

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io

apax

A flexible and performant framework for training machine learning potentials.

acellera-openff-toolkit

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io

pystokes

PyStokes: phoresis and Stokesian hydrodynamics in Python. github.com/rajeshrinet/pystokes

kliff

KIM-based Learning-Integrated Fitting Framework for interatomic potentials.

uf3

UF3: a python library for generating ultra-fast interatomic potentials

q2mm

Quantum to Molecular Mechanics (Q2MM)

neoralab-openff-toolkit

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io

nequip-eqx

JAX implementation of the NequIP neural network interatomic potential