用机器学习加速激光下分子电子动力学模拟，速度快且准。
Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
- 用自回归神经算子做时间演化算子，替代传统数值求解。
- 在多种激光参数下，精度媲美传统方法，速度提升数十倍。
- 适合快速模拟不同激光条件下的分子响应，实验设计利器。
时变密度泛函理论（TDDFT）是研究外加时变扰动（如激光场）下电子动力学的常用方法。本文提出一种基于自回归神经算子的机器学习方法，作为实时时域TDDFT中电子密度的时间演化算子，以加速电子动力学模拟。通过引入物理信息约束与特征化处理，并利用高分辨率训练数据，该模型在精度和计算速度上均优于传统数值求解器。我们在一系列一维双原子分子上验证了模型在多种激光参数下的有效性。该方法有望实现对激光辐照分子与材料的实时建模，借助机器学习预测在广泛变化的激光实验参数空间中快速生成结果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。