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

Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials

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

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

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

[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

Build neural networks for machine learning force fields with JAX

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

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

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

[TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Quantum to Molecular Mechanics (Q2MM)

A flexible and performant framework for training machine learning potentials.

Optimization tool for calibrating coarse-grained force fields of lipids, relying on the simultaneous usage of reference AA trajectories (bottom-up) and experimental data (top-down)

Data and scripts relevant to an evaluation of force field methods for conformer scoring

Official PyTorch implementation of "Comprehensive Molecular Representation from Equivariant Transformer" paper https://arxiv.org/abs/2308.10752. Made in Cardiff University.

Flexible python code to accelerate the application of foundation force fields for their use in surface science and catalysis.

Strategy MMIC for force field parameter assignment

Ridge-regression Atomistic Force Fields in PYthon