txie-93 / cgcnnLinks
Crystal graph convolutional neural networks for predicting material properties.
☆808Updated 4 years ago
Alternatives and similar repositories for cgcnn
Users that are interested in cgcnn are comparing it to the libraries listed below
Sorting:
- Graph deep learning library for materials☆492Updated this week
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆549Updated 2 years ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆989Updated 2 weeks ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆351Updated last year
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆292Updated 4 months ago
- NequIP is a code for building E(3)-equivariant interatomic potentials☆843Updated last week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆352Updated 2 months ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆895Updated last week
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆595Updated last week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆445Updated last month
- 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
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆455Updated 3 months ago
- Deep neural networks for density functional theory Hamiltonian.☆309Updated last year
- SchNet - a deep learning architecture for quantum chemistry☆278Updated 7 years ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆371Updated last week
- Data mining for materials science☆566Updated this week
- An open-source Python package for creating fast and accurate interatomic potentials.☆340Updated 3 months ago
- MatDeepLearn, package for graph neural networks in materials chemistry☆199Updated 2 years ago
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆346Updated 2 years ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆449Updated last week
- A code to generate atomic structure with symmetry☆351Updated 2 weeks ago
- A toolkit for visualizations in materials informatics.☆289Updated last month
- A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.☆349Updated 9 months ago
- Sample codes for my book on molecular dynamics simulation☆271Updated 3 weeks ago
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆272Updated last month
- Neural Network Force Field based on PyTorch☆284Updated 3 months ago
- A Python package for manipulating atomistic data of software in computational science☆238Updated last week
- Training neural network potentials☆455Updated last week
- Matbench: Benchmarks for materials science property prediction☆179Updated last year
- n2p2 - A Neural Network Potential Package☆240Updated 9 months ago