txie-93 / cgcnn
Crystal graph convolutional neural networks for predicting material properties.
☆659Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for cgcnn
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆508Updated last year
- Graph deep learning library for materials☆277Updated this week
- SchNetPack - Deep Neural Networks for Atomistic Systems☆789Updated last week
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆244Updated 3 months ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆236Updated this week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆404Updated this week
- MatDeepLearn, package for graph neural networks in materials chemistry☆175Updated last year
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆369Updated last week
- NequIP is a code for building E(3)-equivariant interatomic potentials☆637Updated this week
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆549Updated this week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆253Updated this week
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆398Updated this week
- SchNet - a deep learning architecture for quantum chemistry☆228Updated 6 years ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆308Updated this week
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆229Updated 4 months ago
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆241Updated 2 weeks ago
- Deep neural networks for density functional theory Hamiltonian.☆243Updated last month
- Public repo for Materials API documentation☆139Updated 2 years ago
- A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.☆249Updated 2 months ago
- Crystal Graph Neural Networks☆104Updated 7 months ago
- An automatic engine for predicting materials properties.☆138Updated last year
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆571Updated last month
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆347Updated this week
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆296Updated last year
- Data mining for materials science☆485Updated this week
- Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction☆90Updated last year
- A repo of examples for the matminer (https://github.com/hackingmaterials/matminer) code☆104Updated 3 years ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆292Updated 3 weeks ago
- A Python package for manipulating atomistic data of software in computational science☆202Updated this week
- Neural Network Force Field based on PyTorch☆238Updated this week