基于机器学习哈密顿量的可扩展光激发诱导分子动力学
Scalable photoexcitation-induced molecular dynamics with machine-learned Hamiltonians
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中文总结 AI 辅助
本文提出TDAP-eML框架，整合机器学习电子结构与原子传播，可模拟光激发诱导的晶格动力学，在大系统中计算成本降低近三个数量级，关联非平衡激发与光诱导力及实验可观测值。
中文摘要 AI 辅助
超快光激发提供了控制固体中结构动力学的可控途径，但预测非平衡电子激发如何驱动跨时空尺度扩展的晶格运动仍是重大计算挑战。本文提出含电子机器学习的时间相关从头算传播（TDAP-eML）框架，该框架将电子演化明确纳入光激发诱导晶格动力学的可扩展模拟中。通过整合机器学习电子结构与原子传播，TDAP-eML可描述光激发如何重塑演化的能量景观及支配结构运动的力。在硅和FeSe等代表性案例中，该框架重现了第一性原理时间相关密度泛函理论计算得到的关键光激发晶格响应，并捕捉了相干声子动力学及其对激发条件的依赖性。其计算优势随系统规模增大而提升，在研究的大系统中计算成本降低近三个数量级。TDAP-eML因此建立了电子与晶格耦合演化的可扩展框架，将非平衡激发与光诱导力、可预测结构动力学及实验可观测值关联起来。
英文摘要
Ultrafast photoexcitation offers a controllable route to steer structural dynamics in solids, yet predicting how nonequilibrium electronic excitation drives lattice motion across extended spatial and temporal scales remains a major computational challenge. Here we introduce time-dependent ab-initio propagation with electronic machine learning (TDAP-eML), a framework that explicitly incorporates electronic evolution into scalable simulations of photoexcitation-induced lattice dynamics. By integrating machine-learned electronic structure with atomistic propagation, TDAP-eML describes how photoexcitation reshapes the evolving energy landscapes and forces governing structural motion. Across representative examples including silicon and FeSe, the framework reproduces key photoexcited lattice responses obtained from first-principles time-dependent density functional theory calculations and captures coherent phonon dynamics together with their dependence on excitation conditions. Its computational advantage increases with system size, reaching nearly three orders of magnitude reduction in computational cost for the large systems examined. TDAP-eML thus establishes a scalable framework for coupled electronic and lattice evolution, linking nonequilibrium excitation to photoinduced forces, predictive structural dynamics, and experimentally accessible observables.
