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O: Fachverband Oberflächenphysik

O 42: Poster Session III: Poster to Mini-Symposium: Machine learning applications in surface science I

O 42.1: Poster

Tuesday, March 2, 2021, 10:30–12:30, P

Learning electron densities in condensed phase space — •Alan Lewis¹, Andrea Grisafi², Michele Ceriotti², and Mariana Rossi¹ — ¹MPI for Structure and Dynamics of Materials, Hamburg, Germany — ²École Polytéchnique Fédèrale de Lausanne, Lausanne, Switzerland

The electron density is a fundamental quantity for modelling and understanding physical phenomena in materials. Not only is it central to theories like density-functional theory, but it also allows the calculation of a wide range of observables that are either directly or indirectly connected to it, like total energies, dipole moments, the electrostatic potential, work functions, and others. In this work, we present a model that is able to learn and predict the electronic density of diverse materials, ranging from liquids to solid semiconductors and metals. This is achieved by extending the framework presented by Grisafi et al [1] to work with periodic boundary conditions and when using a resolution of the identity on a numeric atom-centered orbital basis [2] to obtain coefficients for the expansion of the periodic density. This density is learned through a Gaussian process regression model that makes use of local symmetry-adapted representations of the atomic structure, which makes our method both data-efficient and highly transferable. We discuss the applicability of this model for large-scale periodic systems and its transferability across the periodic table.

[1] Grisafi et al, ACS Cent. Sci. 5, 57-64, 2019

[2] Blum et al, Comput. Phys. Commun. 180, 2175-2196, 2009