Mapping Simulated Two-Dimensional Spectra to Molecular Models Using Machine Learning

Kelsey A. Parker, Jonathan D. Schultz, Niven Singh, Michael R. Wasielewski, David N. Beratan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Two-dimensional (2D) spectroscopy encodes molecular properties and dynamics into expansive spectral data sets. Translating these data into meaningful chemical insights is challenging because of the many ways chemical properties can influence the spectra. To address the task of extracting chemical information from 2D spectroscopy, we study the capacity of simple feedforward neural networks (NNs) to map simulated 2D electronic spectra to underlying physical Hamiltonians. We examined hundreds of simulated 2D spectra corresponding to monomers and dimers with varied Franck-Condon active vibrations and monomer-monomer electronic couplings. We find the NNs are able to correctly characterize most Hamiltonian parameters in this study with an accuracy above 90%. Our results demonstrate that NNs can aid in interpreting 2D spectra, leading from spectroscopic features to underlying effective Hamiltonians.

Original languageEnglish (US)
Pages (from-to)7454-7461
Number of pages8
JournalJournal of Physical Chemistry Letters
Issue number32
StatePublished - Aug 18 2022

ASJC Scopus subject areas

  • Materials Science(all)
  • Physical and Theoretical Chemistry


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