标题： 机器学习时间传播器用于时变密度泛函理论模拟 显示英文标题

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

摘要： 时间依赖密度泛函理论（TDDFT）是一种广泛用于研究在外加时间依赖扰动（如激光场）下电子动力学的方法。 在本工作中，我们提出了一种新方法，利用自回归神经算子作为电子密度的时间传播器，以加速基于实时TDDFT的电子动力学模拟。 通过利用物理信息约束和特征化，以及高分辨率训练数据，我们的模型相比传统数值求解器实现了更高的精度和计算速度。 我们在一系列一维双原子分子上展示了我们模型的有效性，这些分子受到多种激光参数的影响。 该方法在实现受激光照射的分子和材料的实时、即时建模方面具有潜力，适用于不同的实验参数。 显示英文摘要

摘要： 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 novel 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 real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters.