CederGroupHub / chgnetLinks
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
☆343Updated 3 weeks ago
Alternatives and similar repositories for chgnet
Users that are interested in chgnet are comparing it to the libraries listed below
Sorting:
- Graph deep learning library for materials☆464Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆287Updated 2 months ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆201Updated this week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆302Updated 7 months ago
- MACE foundation models (MP, OMAT, Matpes)☆164Updated last week
- A toolkit for visualizations in materials informatics.☆281Updated last week
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆199Updated last week
- An open-source Python package for creating fast and accurate interatomic potentials.☆339Updated 2 months ago
- Deep neural networks for density functional theory Hamiltonian.☆298Updated last year
- atomate2 is a library of computational materials science workflows☆249Updated this week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆437Updated 2 weeks ago
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆577Updated this week
- A Python package for manipulating atomistic data of software in computational science☆235Updated this week
- A code to generate atomic structure with symmetry☆345Updated last week
- Matbench: Benchmarks for materials science property prediction☆174Updated last year
- An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix☆139Updated 2 weeks ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆451Updated last month
- n2p2 - A Neural Network Potential Package☆241Updated 8 months ago
- This add-on to pymatgen provides tools for analyzing diffusion in materials.☆129Updated this week
- doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defec…☆221Updated last week
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆930Updated last week
- New API client for the Materials Project☆150Updated this week
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆368Updated last week
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆440Updated 2 months ago
- Computational tools for simulation of high-entropy alloy surfaces☆18Updated 5 months ago
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆270Updated 2 weeks ago
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆152Updated last year
- ☆258Updated 3 weeks ago
- A collection of Neural Network Models for chemistry☆172Updated last month
- MatDeepLearn, package for graph neural networks in materials chemistry☆198Updated 2 years ago