{"id":13477099,"url":"https://github.com/materialsvirtuallab/maml","last_synced_at":"2025-05-14T21:07:38.983Z","repository":{"id":38333359,"uuid":"236185220","full_name":"materialsvirtuallab/maml","owner":"materialsvirtuallab","description":"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.","archived":false,"fork":false,"pushed_at":"2025-05-01T00:44:54.000Z","size":154122,"stargazers_count":408,"open_issues_count":9,"forks_count":85,"subscribers_count":19,"default_branch":"master","last_synced_at":"2025-05-01T01:35:18.587Z","etag":null,"topics":["deep-learning","machine-learning","machine-learning-force-field","materials-discovery","materials-science","spectroscopy"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":["Jupyter Notebook","General Tools","Machine Learning"],"sub_categories":[],"readme":"\u003cimg src=\"https://github.com/materialsvirtuallab/maml/blob/master/resources/logo_horizontal.png?raw=true\" alt=\"maml\" width=\"50%\"\u003e\n\n[![GitHub license](https://img.shields.io/github/license/materialsvirtuallab/maml)](https://github.com/materialsvirtuallab/maml/blob/main/LICENSE)\n[![Linting](https://github.com/materialsvirtuallab/maml/workflows/Linting/badge.svg)](https://github.com/materialsvirtuallab/maml/workflows/Linting/badge.svg)\n[![Testing](https://github.com/materialsvirtuallab/maml/workflows/Testing/badge.svg)](https://github.com/materialsvirtuallab/maml/workflows/Testing/badge.svg)\n[![Downloads](https://pepy.tech/badge/maml)](https://pepy.tech/project/maml)\n[![codecov](https://codecov.io/gh/materialsvirtuallab/maml/branch/master/graph/badge.svg?token=QNL1CRLVVL)](https://codecov.io/gh/materialsvirtuallab/maml)\n\nmaml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML\nfor materials science as easy as possible.\n\nThe goal of maml is not to duplicate functionality already available in other packages. maml relies on well-established\npackages such as scikit-learn and tensorflow for implementations of ML algorithms, as well as other materials science\npackages such as [pymatgen](http://pymatgen.org) and [matminer](http://hackingmaterials.lbl.gov/matminer/) for\ncrystal/molecule manipulation and feature generation.\n\nOfficial documentation at https://materialsvirtuallab.github.io/maml/\n\n# Features\n\n1. Convert materials (crystals and molecules) into features. In addition to common compositional, site and structural\n   features, we provide the following fine-grain local environment features.\n\n a) Bispectrum coefficients\n b) Behler Parrinello symmetry functions\n c) Smooth Overlap of Atom Position (SOAP)\n d) Graph network features (composition, site and structure)\n\n2. Use ML to learn relationship between features and targets. Currently, the `maml` supports `sklearn` and `keras`\n   models.\n\n3. Applications:\n\n a) `pes` for modelling the potential energy surface, constructing surrogate models for property prediction.\n\n  i) Neural Network Potential (NNP)\n  ii) Gaussian approximation potential (GAP) with SOAP features\n  iii) Spectral neighbor analysis potential (SNAP)\n  iv) Moment Tensor Potential (MTP)\n\n b) `rfxas` for random forest models in predicting atomic local environments from X-ray absorption spectroscopy.\n\n c) `bowsr` for rapid structural relaxation with bayesian optimization and surrogate energy model.\n\n# Installation\n\nPip install via PyPI:\n\n```bash\npip install maml\n```\n\nTo run the potential energy surface (pes), lammps installation is required you can install from source or from `conda`::\n\n```bash\nconda install -c conda-forge/label/cf202003 lammps\n```\n\nThe SNAP potential comes with this lammps installation. The GAP package for GAP and MLIP package for MTP are needed to run the corresponding potentials. For fitting NNP potential, the `n2p2` package is needed.\n\nInstall all the libraries from requirements.txt file::\n\n```bash\npip install -r requirements.txt\n```\n\nFor all the requirements above:\n\n```bash\npip install -r requirements-ci.txt\npip install -r requirements-optional.txt\npip install -r requirements-dl.txt\npip install -r requirements.txt\n```\n\n# Usage\n\nMany Jupyter notebooks are available on usage. See [notebooks](/notebooks). We also have a tool and tutorial lecture\nat [nanoHUB](https://nanohub.org/resources/maml).\n\n# API documentation\n\nSee [API docs](https://materialsvirtuallab.github.io/maml/maml.html).\n\n# Citing\n\n```txt\n@misc{\n    maml,\n    author = {Chen, Chi and Zuo, Yunxing, Ye, Weike, Ji, Qi and Ong, Shyue Ping},\n    title = {{Maml - materials machine learning package}},\n    year = {2020},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https://github.com/materialsvirtuallab/maml}},\n}\n```\n\nFor the ML-IAP package (`maml.pes`), please cite::\n\n```txt\nZuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.;\nWood, M. A.; Ong, S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials.\nJ. Phys. Chem. A 2020, 124 (4), 731–745. https://doi.org/10.1021/acs.jpca.9b08723.\n```\n\nFor the BOWSR package (`maml.bowsr`), please cite::\n\n```txt\nZuo, Y.; Qin, M.; Chen, C.; Ye, W.; Li, X.; Luo, J.; Ong, S. P. Accelerating Materials Discovery with Bayesian\nOptimization and Graph Deep Learning. Materials Today 2021, 51, 126–135.\nhttps://doi.org/10.1016/j.mattod.2021.08.012.\n```\n\nFor the AtomSets model (`maml.models.AtomSets`), please cite::\n\n```txt\nChen, C.; Ong, S. P. AtomSets as a hierarchical transfer learning framework for small and large materials\ndatasets. Npj Comput. Mater. 2021, 7, 173. https://doi.org/10.1038/s41524-021-00639-w\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaterialsvirtuallab%2Fmaml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaterialsvirtuallab%2Fmaml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaterialsvirtuallab%2Fmaml/lists"}