{"id":13698684,"url":"https://github.com/mala-project/mala","last_synced_at":"2025-12-14T17:03:08.465Z","repository":{"id":38107010,"uuid":"353335620","full_name":"mala-project/mala","owner":"mala-project","description":"Materials Learning Algorithms. 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Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations.\n\nMALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS). See [Contributing](docs/source/CONTRIBUTE.md) for contributing code to the repository.\n\nThis repository is structured as follows:\n```\n├── examples : contains useful examples to get you started with the package\n├── install : contains scripts for setting up this package on your machine\n├── mala : the source code itself\n├── test : test scripts used during development, will hold tests for CI in the future\n└── docs : Sphinx documentation folder\n```\n\n## Installation\n\n\u003e **WARNING**: Even if you install MALA via PyPI, please consult the full installation instructions afterwards. External modules (like the QuantumESPRESSO bindings) are not distributed via PyPI!\n\nPlease refer to [Installation of MALA](docs/source/install/installing_mala.rst).\n\n## Running\n\nYou can familiarize yourself with the usage of this package by running\nthe examples in the `example/` folder.\n\n## Contributors\n\nMALA is jointly maintained by \n\n- [Sandia National Laboratories](https://www.sandia.gov/) (SNL), USA.\n    - Scientific supervisor: Sivasankaran Rajamanickam, code maintenance: \nJon Vogel\n- [Center for Advanced Systems Understanding](https://www.casus.science/) (CASUS), Germany.\n    - Scientific supervisor: Attila Cangi, code maintenance: Lenz Fiedler\n\nA full list of contributors can be found [here](docs/source/CONTRIBUTE.md).\n\n## Citing MALA\n\nIf you publish work which uses or mentions MALA, please cite this repository and the following papers:\n\n- A. Cangi, L. Fiedler, B. Brzoza, K. Shah, T. J. Callow, D. Kotik, S. Schmerler, M. C. Barry, J. M. Goff, A. Rohskopf, D. J. Vogel, N. Modine, A. P. Thompson, S. Rajamanickam,\nMaterials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations,\n[Comp. Phys. Commun. 314, 109654 (2025)](https://doi.org/10.1016/j.cpc.2025.109654).\n\n- J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson,\nA. Cangi, S. Rajamanickam, Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks,\n[Phys. Rev. B 104, 035120 (2021)](https://doi.org/10.1103/PhysRevB.104.035120).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmala-project%2Fmala","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmala-project%2Fmala","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmala-project%2Fmala/lists"}