Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
<|reference_start|>Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators: 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.<|reference_end|>
@article{shah2024accelerating,
    title={Accelerating Electron Dynamics Simulations through Machine Learned Time
  Propagators},
    author={Karan Shah, Attila Cangi},
    journal={arXiv preprint arXiv:2407.09628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09628},
    primaryClass={cond-mat.mtrl-sci cs.LG physics.comp-ph}
}