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

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.