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Name: materials-learning-algorithms
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Summary: Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
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![image](./docs/source/img/logos/mala_horizontal.png)

# MALA

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[![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/README.md).

## Running

You can familiarize yourself with the usage of this package by running
the examples in the `example/` folder.

## Institutions
### Founding Institutions

- [Sandia National Laboratories](https://www.sandia.gov/) (SNL), USA.
- [Center for Advanced Systems Understanding](https://www.casus.science/) (CASUS), Germany.

### Contributing Institutions

- [Oak Ridge National Laboratory](https://www.ornl.gov/) (ORNL), USA

## Developers
### Scientific Supervision
- Attila Cangi (CASUS)
- Siva Rajamanickam (SNL)

### Core Developers

- Austin Ellis (ORNL)
- Lenz Fiedler (CASUS)
- Daniel Kotik (CASUS)
- Normand Modine (SNL)
- Vladyslav Oles (ORNL)
- Gabriel Popoola (SNL)
- Aidan Thompson (SNL)
- Steve Schmerler (HZDR)
- Adam Stephens (SNL)

### Contributors

- Sneha Verma (CASUS)
- Parvez Mohammed (CASUS)
- Nils Hoffmann (CASUS)
- Omar Faruk (CASUS)
- Somashekhar Kulkarni (CASUS)

## 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.


