Two-Dimensional Electronic Spectroscopy in the Condensed Phase Using Equivariant Transformer Accelerated Molecular Dynamics Simulations

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.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2025-05-28
DOI
https://doi.org/10.1021/acs.jpclett.5c00911
Citations
4
Primary Topic
Spectroscopy and Quantum Chemical Studies
Type
article
Field-Weighted Citation Impact
4.99

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article

Two-Dimensional Electronic Spectroscopy in the Condensed Phase Using Equivariant Transformer Accelerated Molecular Dynamics Simulations

Christine M. Isborn, Gabriela S. Schlau‐Cohen, Michael S. Chen, Tim Joachim Zuehlsdorff et al.
4 citations
The Journal of Physical Chemistry Letters
Spectroscopy and Quantum Chemical Studies
4.99
article

Two-Dimensional Electronic Spectroscopy in the Condensed Phase Using Equivariant Transformer Accelerated Molecular Dynamics Simulations

Christine M. Isborn, Gabriela S. Schlau‐Cohen, Michael S. Chen, Tim Joachim Zuehlsdorff, Andrés Montoya−Castillo, Thomas E. Markland, Minjung Son, Angela Lee, Andrew Snider, Prachi Gupta, Frank Hu, Joseph Kelly, A. Damiani
article en
4 citations

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.

The Journal of Physical Chemistry LettersVol. 16(22)
Boston University (US), Oregon State University (US), University of California, Merced (US), University of Colorado Boulder (US), University of Colorado System (US), New York University (US), Massachusetts Institute of Technology (US), Stanford University (US)
National Energy Research Scientific Computing Center, Division of Chemistry, Basic Energy Sciences
Affordable and clean energy
Openalex Percentile: Top 5%
Spectroscopy and Quantum Chemical Studies
4.99
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