physics.chem-phOct 7, 2026

PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians

Authors: Rongzhi Gao, Shuguang Chen, Yang Zhou, GuanHua Chen, Ziyang Hu, ChiYung Yam

Organizations: Department of Chemistry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China · Hong Kong Quantum AI Lab, Pak Shek Kok, Hong Kong SAR, China · MattVerse Limited, Pak Shek Kok, Hong Kong SAR, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China

Abstract

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.

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