Shen-Group / E2GNNLinks
☆23Updated last year
Alternatives and similar repositories for E2GNN
Users that are interested in E2GNN are comparing it to the libraries listed below
Sorting:
- A flexible workflow for on-the-fly learning of interatomic potential models.☆31Updated this week
- ML potentials via transfer learning☆22Updated this week
- ☆109Updated this week
- This is the source code for paper "Neural Network Potentials for Accelerated Metadynamics of Oxygen Reduction Kinetics at Au-Water Interf…☆23Updated last year
- A molecular simulation package integrating MLFFs in MOFs for DAC☆40Updated 2 months ago
- Active Learning for Machine Learning Potentials☆62Updated 3 weeks ago
- ☆32Updated 2 months ago
- ☆17Updated 4 months ago
- ☆32Updated last week
- Automated creation and manipulation of Chemical Reaction Networks (CRNs) in heterogeneous catalysis, allowing the evaluation of species a…☆41Updated last week
- ☆15Updated 2 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆77Updated last week
- Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages☆24Updated 2 months ago
- PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…☆54Updated last month
- OVITO Python modifier to compute the Warren-Cowley parameters.☆36Updated 8 months ago
- A fast and accurate model to estimate DFT quality partial atomic charges of periodic materials☆28Updated 4 months ago
- Original implementation of CSPML☆29Updated 11 months ago
- MACE_Osaka24 models☆23Updated 11 months ago
- DistMLIP: A Distributed Inference Library for Fast, Large Scale Atomistic Simulation☆90Updated 2 months ago
- Wyckoff Inorganic Crystal Generator Framework☆26Updated 9 months ago
- Metadynamics code on the G-space.☆14Updated 3 years ago
- Crystal graph attention neural networks for materials prediction☆28Updated 2 years ago
- Python library for the construction of porous materials using topology and building blocks.☆80Updated 6 months ago
- A graph attention network based model for predicting atomic partial charges in metal-organic frameworks.☆13Updated 3 months ago
- Code for automated fitting of machine learned interatomic potentials.☆133Updated last week
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆41Updated last year
- ☆32Updated 3 months ago
- Machine-Learning-Based Interatomic Potentials for Catalysis: an Universal Catalytic Large Atomic Model☆47Updated last month
- ☆30Updated 2 years ago
- Collection of tutorials to use the MACE machine learning force field.☆50Updated last year