Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators

Autor:	Shah, Karan, Cangi, Attila
Rok vydání:	2024
Předmět:	Condensed Matter - Materials Science Computer Science - Machine Learning Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, we present a novel approach to accelerate real time TDDFT based electron dynamics simulations using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters. Comment: 9 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.09628 Zobrazit plný text záznamu View this record from Arxiv