The MALA package - Transferable and Scalable Electronic Structure Simulations Powered by Machine Learning

Interactions between electrons and nuclei, the principal building blocks of matter, determine all materials properties. Understanding and modeling these interactions therefore is of paramount importance to pressing scientific questions, e.g., in the context of renewable energy solutions or sustainable materials. However, electronic structure simulations often face a trade-off between accuracy and system size . One may simulate materials at quantum-accuracy, but can only do so for a few thousand atoms, even with the most advanced electronic structure tools, such as density functional theory (DFT). Conversely, large-scale simulations suffer from drastically reduced predictive power due to necessary approximations.

The Materials Learning Algorithms (MALA) package tackles this challenge by combining neural networks, physically constrained optimization algorithms, and efficient post-processing routines to construct machine-learning models of DFT (ML-DFT). Unlike existing ML approaches, MALA creates ML-DFT models that completely replace DFT, providing access to both scalar quantities like energies and volumetric information about the electronic structure, such as the electronic density. We have demonstrated that MALA can be used with any number of atoms (successfully tested with 100’000 atoms), covering a wide range of temperatures and pressures. MALA enables a promising approach for materials modeling at unattained scale and accuracy.