AI 中文总结
本文提出实时N²AMD框架，利用等变神经网络预测哈密顿量，实现低成本高精度固体非绝热分子动力学，纠正载流子动力学误差、模拟光致铁电相变等，为非平衡材料设计提供新范式。
AI 中文摘要
模拟电子与原子核的耦合非平衡动力学是化学、物理学和材料科学的核心挑战，支配着从光催化到量子信息等一系列现象。主要瓶颈在于缺乏通用、准确且高效的方法来建模完整的激发态势能面：即多个电子态的势能面、作用力及非绝热耦合。尽管机器学习已推动基态模拟取得革命性进展，并在分子激发态建模中展现出潜力，但针对一般凝聚态体系求解完整多态问题的统一框架仍未实现。本文提出了实时N²AMD（神经网络非绝热分子动力学），这一机器学习框架使固体中的实时非绝热分子动力学成为现实。该框架利用等变神经网络预测体系哈密顿量，以远低于从头算的成本提供激发态能量、作用力及非绝热耦合矢量；关键在于，它支持混合泛函精度的模拟，而这一精度水平此前是常规非绝热分子动力学无法企及的。我们通过三个典型示例展示其能力：纠正MoS₂/WS₂异质结中常规方法预测的载流子动力学的数量级误差、模拟此前无法实现的光致铁电相变、在混合泛函级别捕捉TiO₂中的实时极化子形成。实时N²AMD突破了平衡态理论的局限，为远离平衡态运行的材料的预测性第一性原理设计建立了新范式。
英文摘要
Simulating the coupled, nonequilibrium dynamics of electrons and nuclei is a central challenge in chemistry, physics, and materials science, governing phenomena from photocatalysis to quantum information. The primary bottleneck has been the lack of a general, accurate, and efficient method for modeling the complete excited-state landscape: the potential energy surfaces, forces, and non-adiabatic couplings for multiple electronic states. While machine learning has revolutionized ground-state simulations and shown promise for excited states in molecules, a unified framework that solves the complete multi-state problem for general condensed matter systems has remained elusive. Here we introduce on-the-fly N${^2}$AMD (Neural network NAMD), a machine learning framework that makes on-the-fly NAMD in solids a reality. By employing an equivariant neural network to predict the system Hamiltonian, the framework delivers excited-state energies, forces, and non-adiabatic coupling vectors at a fraction of the cost of ab initio calculations. Crucially, it allows simulations with hybrid functional accuracy, a level of approach previously inaccessible for NAMD. We showcase its capabilities with three topical examples: correcting order-of-magnitude errors in carrier dynamics predicted by conventional procedure in a MoS$_2$/WS$_2$ heterostructure, simulating previously inaccessible photoinduced ferroelectric switching, and capturing real-time polaron formation in TiO$_2$ at the hybrid-functional level. On-the-fly N${^2}$AMD moves beyond the limitations of equilibrium theory, establishing a new paradigm for the predictive, first-principles design of materials operating far from equilibrium.
