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

arXiv: https://arxiv.org/abs/2610.09576 · HTML: https://mlsanity.com/paper/2610.09576/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 1 of its 58 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 1 of 1)

- [e3nn: Euclidean Neural Networks](https://mlsanity.com/paper/2207.09453/) (2022)

## Abstract (arXiv)

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.