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

🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.

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

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

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

python library for atomistic machine learning

A Python library and command line interface for automated free energy calculations

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

Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics

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

FLAME: a library for atomistic modeling environments

PotentialLearning.jl: Optimize your atomistic data and interatomic potential models in your molecular dynamic workflows.

A flexible and performant framework for training machine learning potentials.

Generator of polynomial machine learning potentials

Fitting interatomic potential for molecular dynamics

Provide easy-to-use CESMIX-aligned case studies. Integrate the latest developments of the Julia atomistic ecosystem and state-of-the-art tools.

A Python library for developing machine learning interatomic potentials, based on JAX.

A user package of LAMMPS software enabling simulations using linearized machine learning potentials

Modified Embedded Atom Method with Bond Order (MEAM-BO) implementation in LAMMPS

Automatic Differentiation of Interatomic Potentials with Phonons in Julia: ADIP².jl