发表机构
The University of Hong Kong; Hong Kong Quantum AI Lab; MattVerse Limited; University of Electronic Science and Technology of China; Shenzhen Institute for Advanced Study（香港大学; 香港量子人工智能实验室; MattVerse有限公司; 电子科技大学; 深圳高等研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PEACE方法，通过宇称等变隐式哈密顿量与学习到的电子联络，结合对称性态混合和电子框架变化，准确再现非绝热动力学中的激发态布居和系间窜越，表明更完整地融入物理可提升预测精度。
AI 中文摘要
非绝热分子动力学为光驱动过程提供了机理上的见解，并为太阳能转换、光催化和光开关中分子与材料的设计提供信息。准确描述这些过程需要一种既尊重电子对称性又能将能量与态间耦合一致关联起来的表示。在此，我们引入PEACE，它将宇称等变隐式哈密顿量与学习到的电子联络相结合。受控消融实验揭示了对称允许的态混合与电子框架变化在再现交叉结构和弛豫动力学中的互补作用。PEACE能够紧密地再现来自第一性原理模拟的激发态布居动力学，而其扩展到自旋轨道耦合则使得系间窜越的模拟成为可能。这些结果表明，将底层物理更完整地纳入学习到的电子表示中，能够更准确地预测非绝热动力学。
英文摘要
Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar energy conversion, photocatalysis and photo switching. Accurately describing these processes requires a representation that respects electronic symmetry and consistently relates energies to interstate couplings. Here we introduce PEACE, which combines a parity-equivariant latent Hamiltonian with a learned electronic connection. Controlled ablations reveal the complementary roles of symmetry-allowed state mixing and electronic-frame variation in reproducing crossing structures and relaxation dynamics. PEACE closely reproduces excited-state population dynamics from first-principles simulations, while its extension to spin-orbit coupling enables simulations of intersystem crossing. These results demonstrate that a more complete incorporation of the underlying physics into learned electronic representations leads to more accurate predictions of nonadiabatic dynamics.
