Publikationsrepositorium - Helmholtz-Zentrum Dresden-Rossendorf

Artificial intelligence (AI) has great potential for accelerating electronic structure calculations to hitherto unattainable scales [1]. I will present our recent efforts on accomplishing speeding up Kohn-Sham density functional theory calculations at finite temperature with deep neural networks in terms of our Materials Learning Algorithms framework [2,3] by illustrating results for metals across their melting point. Furthermore, our results towards automated machine-learning save orders of magnitude in computational efforts for finding suitable neural networks and set the stage for large-scale AI-driven investigations [4]. Finally, I will conclude with a preview on our most recent result that enables neural-network-driven electronic structure calculations for systems containing more than 100,000 atoms.

[1] L. Fiedler, K. Shah, M. Bussmann, and A. Cangi, Phys. Rev. Materials 6, 040301, (2022).
[2] A. Cangi et al., MALA, https://doi.org/10.5281/zenodo.5557254 (2021).
[3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, and S. Rajamanickam, Phys. Rev. B 104, 035120 (2021).
[4] o 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). (2022).