Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations

Karan Shah; Attila Cangi

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arXiv · 2508.16554

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations

Karan Shah·Attila Cangi

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Abstract

Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this work, we present a machine learning approach to accelerate electron dynamics simulations based on real time TDDFT using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and featurization, 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 under the influence of a range of laser parameters. This method has potential in enabling on-the-fly modeling of laser-irradiated molecules and materials by utilizing fast machine learning predictions in a large space of varying experimental parameters of the laser.

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cond-mat.mtrl-sci cs.LG physics.comp-ph

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Karan Shah, Attila Cangi. 2025-08-22. Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations. https://arxiv.org/abs/2508.16554

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