Physics-Informed Neural Network Models for Predicting the Electronic Structure of Matter

by Dr Attila Cangi (Center for Advanced System Understanding (CASUS), Görlitz)

Thursday 16 Feb 2023, 16:00 → 17:00 Europe/Berlin

Building 99, Room IV (1st floor) (CFEL/DESY)

In this talk, I will present our recent advancements in utilizing Artificial Intelligence (AI) to significantly enhance the efficiency of electronic structure calculations [1]. In particular, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations atfinite temperatures by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate
substantial gains in calculation speed for metals across their melting point. Furthermore, our implementation of automated machine learning hasresulted in significant savings in computational resources when identifying optimal neural network architectures, thereby laying the foundation forlarge-scale AI-driven investigations [4]. I will also showcase our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. Finally, I will provide an outlook on the potential of physics-informed neural networks for solving time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [6].

[1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6,
040301, (2022).
[2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G.
A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, 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, Phys. Rev. B 104, 035120 (2021).
[4] 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).
[5] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A.
P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022).
[6] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural
Networks as Solvers for the Time-Dependent Schrödinger Equation, NeurIPS Machine Learning and the Physical Sciences, arXiv:2210.12522 (2022).

 

https://xfel.zoom.us/j/92202238146?pwd=dFE4dytLbEN2dTl5Smk2dndNbHQzZz09

Meeting ID: 922 0223 8146
Passcode: 526095