Abstract 2372 Increasing Solvent Molecules in an In-Silico Model of Solvated Systems Reduces Gaussian Properties of the Fluctuation of First Excitation State Energy

Understanding the nature of energy fluctuations in different environments is crucial for developing realistic models and predictions. Linear response approximations to modeling dynamics and spectroscopy often assume Gaussian statistics for the energy gaps of molecules in solution. Our study quantifies the energy gap distributions for various solvated molecular systems, assessing how well they adhere to Gaussian statistics. We investigate how molecules behave in different environments when exposed to light or undergoing chemical changes, primarily using a method called density functional theory (DFT) to predict molecular behavior. Density functional theory focuses on where electrons are likely to be found around atoms, aiding our understanding and prediction of electron cloud behavior, which is crucial for how atoms and molecules interact. We evaluated solvated systems in three ways to address the following questions: (1) Does increasing the number of solvent molecules result in fluctuations becoming more Gaussian? (2) Do different strengths of solute-solvent interactions affect the distribution? (3) How does modeling the solvent environment as classical fixed-point charge with molecular mechanics (MM) vs with quantum mechanics (QM) change the distribution? Using the TeraChem computational chemistry software program, we computed excitation energies using the time-dependent density functional theory method with the CAM-B3LYP functional and 6-31+G* basis set. Our solvated systems consisted of four different chromophores - Cresyl Violet, Nile Red, the chromophore of green fluorescent protein, and the chromophore of photoactive yellow protein - surrounded either by methanol or water solvent molecules. Our initial results indicate that as we increase the number of solvent molecules, the fluctuation of energies becomes less Gaussian, challenging current assumptions in the field. Additionally, when changing the solvent model from MM to QM, we observed that the distributions also become less Gaussian. We would like to express our sincere gratitude to the UCOP MRPI for their generous funding through the California Interfacial Science Institute. Additionally, we acknowledge the support provided by the Leaning Aligned Employment Program, funded by the California Student Aid Commission. This research was made possible through their financial support.