force-fields

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

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

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

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

MACE foundation models (MP, OMAT, mh-1)

List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning

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

Tinker: Software Tools for Molecular Design

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

Tinker-GPU: Next Generation of Tinker with GPU Support

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

[TMLR 2024 J2C Certification] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

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

A flexible and performant framework for training machine learning potentials.

Tracking citations of atomistic simulation engines

Quantum to Molecular Mechanics (Q2MM)