Publikationsrepositorium - Helmholtz-Zentrum Dresden-Rossendorf
1 PublikationAccelerating Time-Dependent Density Functional Theory with Physics-Informed Neural Networks
Abstract
Time-dependent density functional theory (TDDFT) is an important method for simulating dynamical processes in quantum many-body systems. We explore the feasibility of physics-informed neural networks as a surrogate for TDDFT. We examine the computational efficiency and convergence behaviour of these solvers to state-of-the-art numerical techniques on models and small molecular systems. The method developed here has the potential to accelerate the TDDFT workflow, enabling the simulation of large-scale calculations of electron dynamics in matter exposed to strong electromagnetic fields, high temperatures, and pressures.
Keywords: physics-informed machine learning; time-dependent density functional theory
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Vortrag (Konferenzbeitrag)
APS March Meeting, 16.03.2022, Chicago, USA
Permalink: https://www.hzdr.de/publications/Publ-35347