Machine Learned Interatomic Potentials to Predict Solvatochromic and Stokes Shifts

Accurate simulations of the excited state dynamics of chromophores in complex environments are a prerequisite to understanding important properties such as photostability, relaxation pathways, and excited state lifetimes. An atomistic understanding of these properties can aid in the design and study of useful chemical compounds such as novel sunscreen candidates. Conflicting requirements set by the need to follow individual trajectories over long timescales, to sample over the ensemble of solvent configurations, and to use a high level of theory to obtain good chemical accuracy puts such methods out of the reach of DFT or QC methods alone. Machine learning allows us to accelerate dynamics simulations of chromophores, including Methyl Anthranilate in a variety of solvent environments, for ground and excited states, while maintaining the accuracy of DFT and higher-level methods. Our workflow to predict peak positions and widths, and solvatochromic and stokes’ shifts, is based on ESTEEM: Explicit Solvent Toolkit for Electronic Excitations of Molecules [1].

[1] esteem.readthedocs.io