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MM: Fachverband Metall- und Materialphysik
MM 53: Data Driven Material Science: Big Data and Workflows VI
MM 53.1: Talk
Thursday, March 21, 2024, 10:15–10:30, C 243
Scalable machine learning for predicting the electronic structure of matter — •Attila Cangi1,2, Lenz Fiedler1,2, Bartosz Brzoza1,2, Karan Shah1,2, Tim Callow1,2, and Steve Schmerler1 — 1Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany — 2Center for Advanced Systems Understanding, Görlitz, Germany
I will present our recent progress in significantly speeding up density functional theory calculations with machine learning [1], for which we have developed the Materials Learning Algorithms framework [2]. Our findings illustrate significant improvements in calculation speed for metals at their melting point. Additionally, our use of automated machine learning has yielded significant reductions in computational resources required to identify optimal neural network architectures [3]. Most importantly, I will present our latest breakthrough, which enables fast neural-network driven electronic structure calculations for systems unattainable by conventional density functional theory calculations [4].
[1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials, 6, 040301 (2022). [2] J. Ellis, L. Fiedler, G. Popoola, N. Modine, J. Stephens, A. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B, 104, 035120 (2021). [3] L. Fiedler, N. Hoffmann, P. Mohammed, G. Popoola, T. Yovell, V. Oles, J. Austin Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol., 3, 045008 (2022). [4] L. Fiedler, N. Modine, S. Schmerler, D. Vogel, G. Popoola, A. Thompson, S. Rajamanickam, A. Cangi, npj. Comput. Mater., 9, 115 (2023).
Keywords: machine learning; neural networks; density functional theory; electronic structure theory