NequIP is a code for building E(3)-equivariant interatomic potentials
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Updated
Jul 25, 2024 - Python
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'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
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
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
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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.
Modified Embedded Atom Method with Bond Order (MEAM-BO) implementation in LAMMPS
Automatic Differentiation of Interatomic Potentials with Phonons in Julia: ADIP².jl
A Python library for developing machine learning interatomic potentials, based on Google JAX.
A python package for fast building amorphous solids and liquid mixtures from https://materialsproject.org computed structures and machine learning interatomic potentials
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