Data-driven Surrogate Modeling of Matter under Extreme Conditions with the Materials Learning Algorithms Package (MALA)

The successful characterization of high energy density (HED) phenomena in experimental facilities is possible only with numerical modeling. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory (density functional theory) capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.
In this talk, I will present a solution to this problem in terms of a data-driven modeling framework for matter under extreme conditions – the Materials Learning Algorithms (MALA) package. MALA generates surrogate models based on deep neural networks that reproduce the output of density functional theory calculations at a fraction of the computational cost. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
MALA is modular and open source. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Center for Advanced Systems Understanding (CASUS), Sandia National Laboratories (SNL), and Oak Ridge National Laboratory (ORNL).