Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

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: 6 months ago
JSON representation

Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.

Awesome Lists containing this project

README 

          
![image](./docs/source/img/logos/mala_horizontal.png)



# MALA



[![CPU](https://github.com/mala-project/mala/actions/workflows/cpu-tests.yml/badge.svg)](https://github.com/mala-project/mala/actions/workflows/cpu-tests.yml)

[![image](https://github.com/mala-project/mala/actions/workflows/gh-pages.yml/badge.svg)](https://mala-project.github.io/mala/)

[![image](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5557255.svg)](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 this repository and the following papers:



- 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,

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations,

[Comp. Phys. Commun. 314, 109654 (2025)](https://doi.org/10.1016/j.cpc.2025.109654).



- J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson,

A. Cangi, S. Rajamanickam, 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).