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

Rongzhi Gao, Shuguang Chen, Yang Zhou, GuanHua Chen, Ziyang Hu +1 more
10/7/2026
physics.chem-phcs.LG

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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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Cite this paper

@article{gao2026peace,
  title  = {PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians},
  author = {Rongzhi Gao and Shuguang Chen and Yang Zhou and GuanHua Chen and Ziyang Hu and ChiYung Yam},
  year   = {2026},
  eprint = {2610.09576},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.09576v1}
}

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