RishikeshMagar / OGCNNLinks
Crystal graph convolutional neural networks for predicting material properties.
☆35Updated 2 years ago
Alternatives and similar repositories for OGCNN
Users that are interested in OGCNN are comparing it to the libraries listed below
Sorting:
- Representation Learning from Stoichiometry☆58Updated 2 years ago
- Crystal Edge Graph Attention Neural Network☆23Updated last year
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- ☆33Updated last year
- Machine Learning Interatomic Potential Predictions☆93Updated last year
- Predict materials properties using only the composition information!☆112Updated 2 years ago
- Composition-Conditioned Crystal GAN pytorch code☆43Updated 3 years ago
- ☆101Updated last week
- Metadynamics code on the G-space.☆14Updated 3 years ago
- Official implementation of DeepDFT model☆84Updated 2 years ago
- ☆20Updated 11 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆74Updated last month
- FTCP code☆35Updated 2 years ago
- Python library for the construction of porous materials using topology and building blocks.☆77Updated 4 months ago
- Code for automated fitting of machine learned interatomic potentials.☆126Updated 2 weeks ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆79Updated 3 years ago
- Active Learning for Machine Learning Potentials☆59Updated 2 months ago
- Code Repository for "Direct prediction of phonon density of states with Euclidean neural network"☆28Updated 3 years ago
- Wyckoff Inorganic Crystal Generator Framework☆25Updated 7 months ago
- Crystal graph attention neural networks for materials prediction☆28Updated 2 years ago
- scripts to load all data from ICSD, Materials Project, and OQMD☆66Updated 3 years ago
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated 2 years ago
- Original implementation of CSPML☆27Updated 10 months ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆81Updated 3 years ago
- Calculate Spectrum Based on Fast Fourier Transform (FFT) of the Velocity Autocorrelation Function (VACF).☆26Updated 2 years ago
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆53Updated last month
- Graph neural network potential with charge transfer☆36Updated 3 years ago
- Python package to interact with high-dimensional representations of the chemical elements☆46Updated this week
- We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …☆19Updated 3 years ago
- ☆30Updated 3 weeks ago