Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators

• URL: http://arxiv.org/abs/2407.09628v2
• Date: Thu, 25 Jul 2024 20:30:43 GMT
• Title: Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
• Authors: Karan Shah, Attila Cangi,
• Abstract summary: We present a novel approach to accelerate real time TDDFT based electron dynamics simulations. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials.
• Score: 0.9208007322096533
• 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 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.

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