CederGroupHub / chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
☆285Updated 2 months ago
Alternatives and similar repositories for chgnet:
Users that are interested in chgnet are comparing it to the libraries listed below
- Graph deep learning library for materials☆315Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆258Updated last month
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆155Updated this week
- atomate2 is a library of computational materials science workflows☆194Updated this week
- doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defec…☆163Updated this week
- A code to generate atomic structure with symmetry☆301Updated last week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆393Updated last week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆277Updated last week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆418Updated 2 months ago
- A toolkit for visualizations in materials informatics.☆192Updated this week
- n2p2 - A Neural Network Potential Package☆233Updated 3 weeks ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆132Updated this week
- A Python package for manipulating atomistic data of software in computational science☆205Updated this week
- This add-on to pymatgen provides tools for analyzing diffusion in materials.☆107Updated this week
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆239Updated 7 months ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆311Updated last month
- CrySPY is a crystal structure prediction tool written in Python.☆124Updated 9 months ago
- New API client for the Materials Project☆129Updated this week
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆145Updated 8 months ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆640Updated this week
- Matbench: Benchmarks for materials science property prediction☆143Updated 6 months ago
- Heavyweight plotting tools for ab initio calculations☆215Updated last month
- An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix☆83Updated last week
- molSimplify code☆182Updated this week
- automatic generation of LAMMPS input files for molecular dynamics simulations of MOFs☆143Updated last year
- i-PI: a universal force engine☆248Updated last week
- Machine Learning Interatomic Potential Predictions☆89Updated last year
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆451Updated last week
- MACE-MP models☆78Updated last month
- MSE5540/6640 Materials Informatics course at the University of Utah. Learn how data science tools are revolutionizing materials science!☆137Updated last week