mir-group / allegroLinks
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
☆440Updated 2 months ago
Alternatives and similar repositories for allegro
Users that are interested in allegro are comparing it to the libraries listed below
Sorting:
- NequIP is a code for building E(3)-equivariant interatomic potentials☆819Updated this week
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆930Updated last week
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆577Updated this week
- Torch-native, batchable, atomistic simulations.☆353Updated this week
- Neural Network Force Field based on PyTorch☆281Updated 2 months ago
- Graph deep learning library for materials☆464Updated this week
- An open-source Python package for creating fast and accurate interatomic potentials.☆339Updated 2 months ago
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆343Updated 3 weeks ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆199Updated last week
- SchNet - a deep learning architecture for quantum chemistry☆270Updated 7 years ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆451Updated last month
- ORB forcefield models from Orbital Materials☆513Updated last week
- Training neural network potentials☆447Updated 2 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆670Updated 10 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆437Updated 2 weeks ago
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆302Updated 7 months ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆201Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆287Updated 2 months ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆368Updated last week
- A code to generate atomic structure with symmetry☆345Updated this week
- cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffD…☆324Updated this week
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆344Updated last year
- A toolkit for visualizations in materials informatics.☆279Updated last week
- Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used…☆182Updated 7 months ago
- DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable implementation of molecular…☆181Updated 2 months ago
- n2p2 - A Neural Network Potential Package☆241Updated 8 months ago
- Matbench: Benchmarks for materials science property prediction☆174Updated last year
- i-PI: a universal force engine☆279Updated last month
- MACE foundation models (MP, OMAT, Matpes)☆164Updated last week
- atomate2 is a library of computational materials science workflows☆249Updated this week