  Two-dimensional electronic spectroscopy has become one of the main
experimental tools for analyzing the dynamics of excitonic energy transfer in
large molecular complexes. Simplified theoretical models are usually employed
to extract model parameters from the experimental spectral data. Here we show
that computationally expensive but exact theoretical methods encoded into a
neural network can be used to extract model parameters and infer structural
information such as dipole orientation from two dimensional electronic spectra
(2DES) or reversely, to produce 2DES from model parameters. We propose to use
machine learning as a tool to predict unknown parameters in the models
underlying recorded spectra and as a way to encode computationally expensive
numerical methods into efficient prediction tools. We showcase the use of a
trained neural network to efficiently compute disordered averaged spectra and
demonstrate that disorder averaging has non-trivial effects for polarization
controlled 2DES.
