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Hydrogen bonding interactions with chromophores in chemical and biologicalenvironments play a key role in determining their electronic absorption andrelaxation processes, which are manifested in their linear and multidimensionaloptical spectra. For chromophores in the condensed phase, the large number ofatoms needed to simulate the environment has traditionally prohibited the useof high-level excited-state electronic structure methods. By leveragingtransfer learning, we show how to construct machine-learned models toaccurately predict high-level excitation energies of a chromophore in solutionfrom only 400 high-level calculations. We show that when the electronicexcitations of the green fluorescent protein chromophore in water are treatedusing EOM-CCSD embedded in a DFT description of the solvent, the opticalspectrum is correctly captured and that this improvement arises from correctlytreating the coupling of the electronic transition to electric fields, whichleads to a larger response upon hydrogen bonding between the chromophore andwater.

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