Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations
Abstract
Two-dimensional electronic spectroscopy (2DES) provides rich information about how the electronic states of molecules, proteins, and solid-state materials interact with each other and their surrounding environment. Atomistic molecular dynamics simulations offer an appealing route to uncover how nuclear motions mediate electronic energy relaxation and their manifestation in electronic spectroscopies, but are computationally expensive. Here we show that, by using an equivariant transformer-based machine learning architecture trained with only ~2500 ground state and ~100 excited state electronic structure calculations, one can construct accurate machine-learned potential energy surfaces for both the ground-state electronic surface and excited-state energy gap. We demonstrate the utility of this approach for simulating the dynamics of Nile blue in ethanol, where we experimentally validate and decompose the simulated 2DES to establish the nuclear motions of the chromophore and the solvent that couple to the excited state, connecting the spectroscopic signals to their molecular origin.
Cite
@article{arxiv.2503.22583,
title = {Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations},
author = {Joseph Kelly and Frank Hu and Arianna Damiani and Michael S. Chen and Andrew Snider and Minjung Son and Angela Lee and Prachi Gupta and Andres Montoya-Castillo and Tim J. Zuehlsdorff and Gabriela S. Schlau-Cohen and Christine M. Isborn and Thomas E. Markland},
journal= {arXiv preprint arXiv:2503.22583},
year = {2025}
}