Combining Vibronic and Environmental Effects with Machine Learning in Simulations of Linear and Nonlinear Optical Spectra: Resolving the Challenge of Modeling the Spectrum of GFP Chromophore in Water

Including both environmental and vibronic effects is important for accurate simulation of optical spectra, but combining these effects remains computationally challenging. This talk will outline two approaches for spectral simulations that consider both the explicit atomistic environment and vibronic transitions. Both phenomena are responsible for spectral shapes in linear spectroscopy and the electronic evolution measured in nonlinear spectroscopy. The first approach utilizes snapshots of chromophore-environment configurations for which chromophore normal modes are determined. The second approach obtains excitation energies for a series of time-correlated snapshots. Both approaches make strides towards more accurate optical spectroscopy simulations. I will show how the approaches can also be made computationally feasible through machine learning of ground and excited state potentials, opening the door to new physical insights of complex condensed phase systems. By combining vibronic and environmental effects, along with machine learning for high level wave function theory, we resolve the long-standing challenge of accurately simulating the linear absorption spectrum of the aqueously solvated GFP chromophore.