hackingmaterials / automatminer
An automatic engine for predicting materials properties.
☆154Updated last year
Alternatives and similar repositories for automatminer:
Users that are interested in automatminer are comparing it to the libraries listed below
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆243Updated 8 months ago
- This add-on to pymatgen provides tools for analyzing diffusion in materials.☆114Updated this week
- A repo of examples for the matminer (https://github.com/hackingmaterials/matminer) code☆110Updated 3 years ago
- atomate is a powerful software for computational materials science and contains pre-built workflows.☆251Updated 8 months ago
- Python package to aid materials design and informatics☆106Updated last week
- The Materials Project Workshop Curriculum☆115Updated 2 years ago
- Automatic generation of crystal structure descriptions.☆113Updated last week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆294Updated 3 weeks ago
- Machine Learning Interatomic Potential Predictions☆90Updated last year
- doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defec…☆177Updated last week
- atomate2 is a library of computational materials science workflows☆205Updated last week
- Matbench: Benchmarks for materials science property prediction☆148Updated 7 months ago
- CrySPY is a crystal structure prediction tool written in Python.☆127Updated 10 months ago
- Graph deep learning library for materials☆330Updated last week
- A suite of computational materials science tools.☆136Updated last year
- Public repo for Materials API documentation☆140Updated 2 years ago
- Benchmark Suite for Machine Learning Interatomic Potentials for Materials☆106Updated 3 years ago
- A machine learning environment for atomic-scale modeling in surface science and catalysis.☆110Updated 9 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆407Updated this week
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆145Updated 9 months ago
- General purpose tools for high-throughput catalysis☆91Updated 9 months ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- A toolkit for visualizations in materials informatics.☆206Updated this week
- ☆67Updated 4 years ago
- Software for generating machine-learning interatomic potentials for LAMMPS☆163Updated this week
- A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/☆80Updated 2 months ago
- Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials Project website.☆167Updated last week
- Electronic transport properties from first-principles calculations☆142Updated last week
- Defect structure-searching employing chemically-guided bond distortions☆90Updated 2 months ago
- Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction☆95Updated 2 years ago