ACEsuit / maceLinks
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
☆989Updated 2 weeks ago
Alternatives and similar repositories for mace
Users that are interested in mace are comparing it to the libraries listed below
Sorting:
- NequIP is a code for building E(3)-equivariant interatomic potentials☆843Updated last week
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆595Updated last week
- Graph deep learning library for materials☆492Updated this week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆352Updated 2 months ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆449Updated last week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆455Updated 3 months ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆340Updated 3 months ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆895Updated last week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆445Updated last month
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆351Updated last year
- Crystal graph convolutional neural networks for predicting material properties.☆808Updated 4 years ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆292Updated 4 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆675Updated 11 months ago
- Torch-native, batchable, atomistic simulations.☆390Updated this week
- Neural Network Force Field based on PyTorch☆284Updated 3 months ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆371Updated last week
- Training neural network potentials☆455Updated last week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆307Updated 8 months ago
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆549Updated 2 years ago
- Semiempirical Extended Tight-Binding Program Package☆733Updated 2 weeks ago
- ORB forcefield models from Orbital Materials☆525Updated last month
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆208Updated 2 weeks ago
- Deep neural networks for density functional theory Hamiltonian.☆309Updated last year
- A toolkit for visualizations in materials informatics.☆289Updated last month
- n2p2 - A Neural Network Potential Package☆240Updated 9 months ago
- MACE foundation models (MP, OMAT, Matpes)☆182Updated last month
- SchNet - a deep learning architecture for quantum chemistry☆278Updated 7 years ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆203Updated last month
- A code to generate atomic structure with symmetry☆351Updated 2 weeks ago
- MatterSim: A deep learning atomistic model across elements, temperatures and pressures.☆497Updated 5 months ago