
RG
Rongzhi Gao, Shuguang Chen, Yang Zhou, GuanHua Chen, Ziyang Hu, ChiYung Yam
· 1 min read
ResearcharXiv cs.LG
PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians
arXiv:2610.09576v1 Announce Type: cross
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.
Original source
This story was published by arXiv cs.LG and written by Rongzhi Gao, Shuguang Chen, Yang Zhou, GuanHua Chen, Ziyang Hu, ChiYung Yam. SyncAI.news shows a preview; the complete article is on the publisher's site.
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