  The excited state dynamics of chromophores in complex environments determine
a range of vital biological and energy capture processes. Time-resolved,
multidimensional optical spectroscopies provide a key tool to investigate these
processes. Although theory has the potential to decode these spectra in terms
of the electronic and atomistic dynamics, the need for large numbers of excited
state electronic structure calculations severely limits first principles
predictions of multidimensional optical spectra for chromophores in the
condensed phase. Here, we leverage the locality of chromophore excitations to
develop machine learning models to predict the excited state energy gap of
chromophores in complex environments for efficiently constructing linear and
multidimensional optical spectra. By analyzing the performance of these models,
which span a hierarchy of physical approximations, across a range of
chromophore-environment interaction strengths, we provide strategies for the
construction of ML models that greatly accelerate the calculation of
multidimensional optical spectra from first principles.
