SKM 2023 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 9: Focus Session: Frontiers of Electronic-Structure Theory I (joint session O/HL)

O 9.4: Vortrag

Montag, 27. März 2023, 11:45–12:00, TRE Ma

Predicting the electronic structure at any length scale with machine learning — •Attila Cangi — Helmholtz-Zentrum Dresden-Rossendorf, Görlitz, Germany

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Calculations rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a scalable machine learning framework for predicting the electronic structure on any length scale [1,2,3]. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions.

[1] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B 104, 035120 (2021). [2] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022). [3] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022).