MilesZhao / PGCGM
Source code for generating materials with 20 space groups using PGCGM
☆32Updated 2 years ago
Alternatives and similar repositories for PGCGM:
Users that are interested in PGCGM are comparing it to the libraries listed below
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆19Updated 7 months ago
- ☆28Updated 5 months ago
- ☆10Updated last year
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆36Updated 11 months ago
- FTCP code☆34Updated last year
- Composition-Conditioned Crystal GAN pytorch code☆43Updated 3 years ago
- ☆32Updated 4 years ago
- Diffusion Probabilistic CDVAE☆23Updated last year
- ☆24Updated 8 months ago
- ☆10Updated 3 months ago
- This is the source code of CubicGAN generating cubic crystal structures using improved WGAN.☆10Updated 2 years ago
- Wyckoff Inorganic Crystal Generator Framework☆21Updated 2 months ago
- Original implementation of CSPML☆24Updated 4 months ago
- [ICLR 2024] The implementation for the paper "Space Group Constrained Crystal Generation"☆40Updated 4 months ago
- A text-guided diffusion model for crystal structure generation☆38Updated 2 months ago
- ☆15Updated 6 months ago
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆18Updated 2 years ago
- Equivariant Diffusion for Crystal Structure Prediction (ICML 2024)☆20Updated 8 months ago
- Official implementation of DeepDFT model☆75Updated 2 years ago
- ☆28Updated 3 years ago
- Learning to Discover Crystallographic Structures with Generative Adversarial Networks☆38Updated 5 years ago
- [NeurIPS 2024] source code for "A Recipe for Charge Density Prediction"☆33Updated 4 months ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- ☆22Updated 2 years ago
- Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.☆37Updated last year
- ☆25Updated last year
- A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w☆68Updated last month
- Generate and predict molecular electron densities with Euclidean Neural Networks☆47Updated last year
- We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …☆18Updated 3 years ago
- Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.☆19Updated 8 months ago