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. I will discuss our recent work in developing accurate and efficient machine learning and quantum dynamics approaches to simulate 2DES. First, I will 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 the excited-state energy gap [1]. I will demonstrate the utility of this approach for simulating the dynamics of the Nile blue chromophore in ethanol, where I will show that we can reproduce and explain the experimentally observed 2DES by decomposing it and thus establish the nuclear motions of the chromophore and the solvent that couple to the excited state, connecting the spectroscopic signals to their molecular origin. I will then highlight our recent work, where we have introduced accurate and efficient nonadiabatic quantum dynamics approaches to simulate 2DES based on pure-state Ehrenfest and spin-mapping methods [2].

[1] Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations. J. Kelly, F. Hu, A. Damiani, M. S. Chen, A. Snider, M. Son, A. Lee, P. Gupta, A. Montoya-Castillo, T. J. Zuehlsdorff, G. S. Schlau-Cohen, C. M. Isborn, and T. E. Markland, J. Phys. Chem. Lett., 16, 5561-5569 (2025)

[2] Two-dimensional Electronic Spectra from Trajectory-based dynamics: Pure-state Ehrenfest and Spin-mapping Approaches. A. Z. Lieberherr, J. Kelly, J. E. Runeson, T. E. Markland, and D. E. Manolopoulos, arXiv:2508.19377 (2025)