DFT Surrogate modeling with the Materials Learning Algorithms (MALA) – Theoretical Background

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. In this talk, an overview over the theoretical background that enables estimation of physical quantities based on the local density of states (LDOS) is given.