PKUsamPHTeam / ASPH-CodeLinks
data and code to reduplicate paper: Topological representations of crystalline compounds for the machine-learning prediction of materials properties
☆22Updated 5 years ago
Alternatives and similar repositories for ASPH-Code
Users that are interested in ASPH-Code are comparing it to the libraries listed below
Sorting:
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆85Updated 4 years ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆43Updated 2 years ago
- Representation Learning from Stoichiometry☆60Updated 3 years ago
- ☆31Updated 4 years ago
- Composition-Conditioned Crystal GAN pytorch code☆42Updated 3 years ago
- image-based generative model for inverse design of solid state materials☆42Updated 3 years ago
- ☆35Updated 3 years ago
- Deep learning for crystal-structure recognition and analysis of atomic structures☆42Updated last year
- Active learning workflow developed as a part of the upcoming article "Machine Learning Inter-Atomic Potentials Generation Driven by Activ…☆28Updated 4 years ago
- This python code creates hybrid MD/MC (NAMD/GOMC) simulations for the NVT, NPT, GCMC, and GEMC-NVT ensembles☆46Updated 2 weeks ago
- Crystal Edge Graph Attention Neural Network☆23Updated last year
- Crystal graph convolutional neural networks for predicting material properties.☆33Updated 3 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.☆38Updated 2 years ago
- Source code for generating materials with 20 space groups using PGCGM☆34Updated 3 years ago
- Machine learning model for crystal lattice constant prediction☆14Updated 4 years ago
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆41Updated last year
- Predict materials properties using only the composition information!☆120Updated 2 years ago
- A system for rapid identification and analysis of metal-organic frameworks☆69Updated 2 months ago
- ☆29Updated 3 years ago
- Learning to Discover Crystallographic Structures with Generative Adversarial Networks☆39Updated 6 years ago
- Active Learning for Machine Learning Potentials☆63Updated this week
- Heat capacity predictor for porous materials☆13Updated last year
- ☆19Updated 8 years ago
- Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022☆53Updated 3 years ago
- For the conversion of crystal systems (as cifs) to LAMMPS inputs☆27Updated 4 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆85Updated 3 years ago
- A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w☆72Updated 3 months ago
- Code Repository for "Direct prediction of phonon density of states with Euclidean neural network"☆28Updated 3 years ago
- ☆24Updated 3 years ago
- Scalable graph neural networks for materials property prediction☆63Updated 2 years ago