Materials Learning Algorithms

MALA 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).

Further information:

https://github.com/mala-project/mala

References:

1. A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, Materials Learning Algorithms (MALA) (2022). [doi.org/10.5281/zenodo.5557254]

2. 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]

3. N. A. Modine, J. A. Stephens, L. P. Swiler, A. P. Thompson, J. D. Vogel, L. Fiedler, A. Cangi, S. Rajamanickam, Accelerating Multiscale Materials Modeling with Machine Learning, Technical Report SAND2022-12875, United States Department of Energy (2022). [doi.org/10.2172/1889336]

4.  L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Training-free hyperparameter optimization of neural networks for electronic structures in matter, Mach. Learn.: Sci. Technol. 3 045008 (2022). [doi.org/10.1088/2632-2153/ac9956]

5. L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, Predicting electronic structures at any length scale with machine learning, arXiv:2210.11343 (2022). [doi.org/10.48550/arXiv.2210.11343]