by256 / icsg3dLinks
3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)
☆42Updated 2 years ago
Alternatives and similar repositories for icsg3d
Users that are interested in icsg3d are comparing it to the libraries listed below
Sorting:
- FTCP code☆36Updated 2 years ago
- Predict materials properties using only the composition information!☆119Updated 2 years ago
- Representation Learning from Stoichiometry☆59Updated 3 years ago
- ☆34Updated last year
- Composition-Conditioned Crystal GAN pytorch code☆43Updated 3 years ago
- Crystal graph convolutional neural networks for predicting material properties.☆34Updated 3 years ago
- Machine Learning Interatomic Potential Predictions☆94Updated last year
- Python library for the construction of porous materials using topology and building blocks.☆80Updated 7 months ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆84Updated 4 years ago
- Learning to Discover Crystallographic Structures with Generative Adversarial Networks☆39Updated 6 years ago
- Automatic generation of crystal structure descriptions.☆126Updated this week
- Crystal Edge Graph Attention Neural Network☆24Updated last year
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆152Updated last year
- The QMOF Database: A database of quantum-mechanical properties for metal-organic frameworks.☆157Updated last month
- ☆66Updated 4 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆82Updated 3 years ago
- Integer Programming encoding for Crystal Structure Prediction with classic and quantum computing bindings☆48Updated 2 years ago
- ☆112Updated 4 months ago
- CrySPY is a crystal structure prediction tool written in Python.☆144Updated 3 months ago
- Universal Transfer Learning in Porous Materials, including MOFs.☆114Updated last year
- image-based generative model for inverse design of solid state materials☆41Updated 3 years ago
- scripts to load all data from ICSD, Materials Project, and OQMD☆68Updated 3 years ago
- We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …☆21Updated 4 years ago
- Official implementation of DeepDFT model☆86Updated 2 years ago
- Code for automated fitting of machine learned interatomic potentials.☆133Updated 3 weeks ago
- A Python library for building atomic neural networks☆121Updated 2 weeks ago
- Workflow for creating and analyzing the Open Catalyst Dataset☆122Updated 11 months ago
- A repository for implementing graph network models based on atomic structures.☆101Updated last year
- Heat capacity predictor for porous materials☆13Updated last year
- A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w☆72Updated 2 months ago