NequIP is a code for building E(3)-equivariant interatomic potentials
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Updated
May 10, 2024 - Python
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
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
[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.
FLAME: a library for atomistic modeling environments
An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.
A flexible and performant framework for training 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
Generator of polynomial machine learning potentials
Automatic Differentiation of Interatomic Potentials with Phonons in Julia: ADIP².jl
A python package for fast building amorphous solids and liquid mixtures from https://materialsproject.org computed structures and machine learning interatomic potentials
Webpage for RANN interatomic potential
Instructions and scripts for adaptive sampling for gas-surface dynamics
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