Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure

Hydrogen bonding interactions with chromophores in chemical
and
biological environments play a key role in determining their electronic
absorption and relaxation processes, which are manifested in their
linear and multidimensional optical spectra. For chromophores in the
condensed phase, the large number of atoms needed to simulate the
environment has traditionally prohibited the use of high-level excited-state
electronic structure methods. By leveraging transfer learning, we
show how to construct machine-learned models to accurately predict
the high-level excitation energies of a chromophore in solution from
only 400 high-level calculations. We show that when the electronic
excitations of the green fluorescent protein chromophore in water
are treated using EOM-CCSD embedded in a DFT description of the solvent
the optical spectrum is correctly captured and that this improvement
arises from correctly treating the coupling of the electronic transition
to electric fields, which leads to a larger response upon hydrogen
bonding between the chromophore and water.