materialsvirtuallab / mamlLinks
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
☆418Updated last week
Alternatives and similar repositories for maml
Users that are interested in maml are comparing it to the libraries listed below
Sorting:
- An open-source Python package for creating fast and accurate interatomic potentials.☆329Updated last month
- Graph deep learning library for materials☆363Updated last week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆438Updated 6 months ago
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆257Updated last week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆316Updated 3 months ago
- A code to generate atomic structure with symmetry☆320Updated this week
- A toolkit for visualizations in materials informatics.☆241Updated last week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆288Updated 3 months ago
- An automatic engine for predicting materials properties.☆158Updated last year
- atomate2 is a library of computational materials science workflows☆225Updated last week
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆517Updated this week
- n2p2 - A Neural Network Potential Package☆236Updated 3 months ago
- libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io☆363Updated 2 months ago
- Curated list of known efforts in materials informatics, i.e. in modern materials science☆451Updated 3 weeks ago
- This repository is no longer maintained. For the latest updates and continued development, please visit: https://github.com/atomgptlab/al…☆273Updated 2 weeks ago
- A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.☆286Updated 3 months ago
- atomate is a powerful software for computational materials science and contains pre-built workflows.☆257Updated 11 months ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆783Updated last week
- Software for generating machine-learning interatomic potentials for LAMMPS☆168Updated last week
- Matbench: Benchmarks for materials science property prediction☆160Updated 10 months ago
- Things that you should (and should not) do in your Materials Informatics research.☆187Updated last year
- i-PI: a universal force engine☆265Updated 2 weeks ago
- doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defec…☆194Updated this week
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆410Updated last week
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆168Updated last week
- A Python package for manipulating atomistic data of software in computational science☆211Updated last week
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆347Updated last week
- A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies☆293Updated 3 months ago
- Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials Project website.☆177Updated last week
- Data mining for materials science☆533Updated last week