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https://github.com/mala-project/mala
Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
https://github.com/mala-project/mala
density-functional-theory dft electronic-structure machine-learning neural-network
Last synced: about 2 months ago
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Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
- Host: GitHub
- URL: https://github.com/mala-project/mala
- Owner: mala-project
- License: bsd-3-clause
- Created: 2021-03-31T11:40:38.000Z (over 3 years ago)
- Default Branch: develop
- Last Pushed: 2024-07-29T19:33:06.000Z (about 2 months ago)
- Last Synced: 2024-07-30T01:44:09.239Z (about 2 months ago)
- Topics: density-functional-theory, dft, electronic-structure, machine-learning, neural-network
- Language: Python
- Homepage: https://mala-project.github.io/mala/
- Size: 56.5 MB
- Stars: 80
- Watchers: 9
- Forks: 25
- Open Issues: 41
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# MALA
[](https://github.com/mala-project/mala/actions/workflows/cpu-tests.yml)
[](https://mala-project.github.io/mala/)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://doi.org/10.5281/zenodo.5557255)
MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations.
MALA 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.
This repository is structured as follows:
```
├── examples : contains useful examples to get you started with the package
├── install : contains scripts for setting up this package on your machine
├── mala : the source code itself
├── test : test scripts used during development, will hold tests for CI in the future
└── docs : Sphinx documentation folder
```
## Installation
> **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!
Please refer to [Installation of MALA](docs/source/install/installing_mala.rst).
## Running
You can familiarize yourself with the usage of this package by running
the examples in the `example/` folder.
## Contributors
MALA is jointly maintained by
- [Sandia National Laboratories](https://www.sandia.gov/) (SNL), USA.
- Scientific supervisor: Sivasankaran Rajamanickam, code maintenance:
Jon Vogel
- [Center for Advanced Systems Understanding](https://www.casus.science/) (CASUS), Germany.
- Scientific supervisor: Attila Cangi, code maintenance: Lenz Fiedler
A full list of contributors can be found [here](docs/source/CONTRIBUTE.md).
## Citing MALA
If you publish work which uses or mentions MALA, please cite the following paper:
J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson,
A. Cangi, S. Rajamanickam (2021). Accelerating Finite-temperature
Kohn-Sham Density Functional Theory with Deep Neural Networks.
[Phys. Rev. B 104, 035120 (2021)](https://doi.org/10.1103/PhysRevB.104.035120)
alongside this repository.