mir-group / allegroLinks
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
β453Updated 3 weeks 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 potentialsβ847Updated last week
- π A ranked list of awesome atomistic machine learning projects βοΈπ§¬π.β603Updated this week
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.β1,005Updated last month
- Neural Network Force Field based on PyTorchβ284Updated 4 months ago
- Torch-native, batchable, atomistic simulations.β397Updated this week
- Graph deep learning library for materialsβ499Updated 2 weeks ago
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.govβ356Updated 2 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorchβ677Updated last week
- An open-source Python package for creating fast and accurate interatomic potentials.β342Updated 4 months ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.β456Updated 3 months ago
- ORB forcefield models from Orbital Materialsβ529Updated last week
- An evaluation framework for machine learning models simulating high-throughput materials discovery.β206Updated this week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.β446Updated 2 weeks ago
- Training neural network potentialsβ461Updated last week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.β311Updated 9 months ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]β353Updated last year
- SchNet - a deep learning architecture for quantum chemistryβ281Updated 7 years ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.β210Updated 2 weeks ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudharyβ294Updated 4 months ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force fieldβ374Updated last week
- A toolkit for visualizations in materials informatics.β294Updated last week
- Matbench: Benchmarks for materials science property predictionβ180Updated last year
- [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationsβ316Updated 11 months ago
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.β325Updated 4 years ago
- DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable implementation of molecularβ¦β185Updated last month
- A code to generate atomic structure with symmetryβ353Updated this week
- OpenMM plugin to define forces with neural networksβ220Updated 10 months ago
- n2p2 - A Neural Network Potential Packageβ240Updated 10 months ago
- MACE foundation models (MP, OMAT, Matpes)β189Updated 2 months ago
- Converts an xyz file to an RDKit mol objectβ290Updated 11 months ago