mir-group / nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
☆686Updated this week
Alternatives and similar repositories for nequip:
Users that are interested in nequip are comparing it to the libraries listed below
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆624Updated this week
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆371Updated 2 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆590Updated last month
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆443Updated this week
- Neural Network Force Field based on PyTorch☆262Updated 2 weeks ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆412Updated 2 months ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆820Updated this week
- Graph deep learning library for materials☆308Updated this week
- Training neural network potentials☆363Updated last week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆273Updated last month
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆324Updated last week
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆308Updated 3 years ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆268Updated 6 months ago
- SchNet - a deep learning architecture for quantum chemistry☆243Updated 6 years ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆309Updated 2 weeks ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆388Updated 2 weeks ago
- GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆192Updated last year
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆251Updated 3 weeks ago
- Accurate Neural Network Potential on PyTorch☆482Updated 3 months ago
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆515Updated last year
- [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations☆247Updated last week
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆148Updated 2 weeks ago
- n2p2 - A Neural Network Potential Package☆231Updated this week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆268Updated 2 weeks ago
- ANI-1 neural net potential with python interface (ASE)☆221Updated 11 months ago
- The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation avai…☆327Updated last week
- OpenMM plugin to define forces with neural networks☆191Updated 3 months ago
- Deep neural networks for density functional theory Hamiltonian.☆259Updated 4 months ago
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆312Updated last year
- DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable implementation of molecular…☆168Updated 4 months ago