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arXiv · 2602.05763

Retrieval of the nuclear motion in a molecule from photoelectron momentum distributions using non-Born-Oppenheimer quantum dynamics and deep learning

N. I. Shvetsov-Shilovski·M. Lein
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Abstract

By using a neural network that takes momentum distributions of photoelectrons produced in strong-field ionization as input, we retrieve the time-dependent bond length of a dissociating one-dimensional H$_{2}^{+}$ molecule. The photoelectron momentum distributions are calculated from the direct numerical solution of the non-Born-Oppenheimer time-dependent Schrödinger equation. We simulate two setups: first, molecules prepared in the first excited electronic state, second, a pump-probe scheme starting from the ground state. We show that in both schemes a neural network trained on momentum distributions calculated for frozen nuclei retrieves the time-dependent bond length with an absolute error of $0.2$-$0.4$ a.u. We find that a neural network, when applied to photoelectron momentum distributions obtained within the pump-probe scheme, can be used for the retrieval of the equilibrium internuclear distance and ground-state population. This opens new perspectives for extracting electronic properties of molecules from electron momentum distributions using deep learning.

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N. I. Shvetsov-Shilovski, M. Lein. 2026-02-05. Retrieval of the nuclear motion in a molecule from photoelectron momentum distributions using non-Born-Oppenheimer quantum dynamics and deep learning. https://arxiv.org/abs/2602.05763

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