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

Fiedler, L.

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.

Keywords: Density Functional Theory; Machine Learning

  • Open Access Logo Lecture (Conference) (Online presentation)
    (TD)DFT Student Seminar Series (#5), 03.08.2021, Newark, USA

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Permalink: https://www.hzdr.de/publications/Publ-33063
Publ.-Id: 33063