First-principles modeling of electronic transport properties and physics-informed machine learning for electron dynamics

In this talk, I will present our research on the application of time-dependent density functional theory (TDDFT) to model observables induced in matter under extreme conditions. Specifically, I will discuss the electron loss function in scattering experiments with X-ray free electron lasers [1] and the electrical conductivity in metals [2, 3]. Additionally, I will explore the potential of physics-informed neural networks for solving the time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [4].

[1] Z. Moldabekov, T. Dornheim, A. Cangi, Sci. Rep. 12, 1093 (2022).
[2] K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, Phys. Rev. B 107, 115131 (2023).
[3] K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, arXiv:2210.10132 (2022).
[4] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, NeurIPS Machine Learning and the Physical Sciences, arXiv:2210.12522 (2022).