materials-learning-algorithms 1.2.1

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

MALA

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 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.

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 (SNL), USA.
  • Scientific supervisor: Sivasankaran Rajamanickam, code maintenance: Jon Vogel
• Center for Advanced Systems Understanding (CASUS), Germany.
  • Scientific supervisor: Attila Cangi, code maintenance: Lenz Fiedler

A full list of contributors can be found here.

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)

alongside this repository.