  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.
