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

• URL: http://arxiv.org/abs/2508.16554v2
• Date: Wed, 24 Sep 2025 12:54:35 GMT
• Title: Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
• Authors: Karan Shah, Attila Cangi,
• Abstract summary: We present a machine learning approach to accelerate electron dynamics simulations based on real time TDDFT.<n>We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules under the influence of a range of laser parameters.
• Score: 0.28647133890966986
• License: http://creativecommons.org/licenses/by/4.0/
• 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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