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

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.

Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)

arXiv:2610.09576 [physics.chem-ph]

(or arXiv:2610.09576v1 [physics.chem-ph] for this version)

https://doi.org/10.48550/arXiv.2610.09576 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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