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The solvation free energy of organic molecules is a critical parameter indetermining emergent properties such as solubility, liquid-phase equilibriumconstants, and pKa and redox potentials in an organic redox flow battery. Inthis work, we present a machine learning (ML) model that can learn and predictthe aqueous solvation free energy of an organic molecule using Gaussian processregression method based on a new molecular graph kernel. To investigate theperformance of the ML model on electrostatic interaction, the nonpolarinteraction contribution of solvent and the conformational entropy of solute insolvation free energy, three data sets with implicit or explicit water solventmodels, and contribution of conformational entropy of solute are tested. Wedemonstrate that our ML model can predict the solvation free energy ofmolecules at chemical accuracy with a mean absolute error of less than 1kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve thegeneral data scarcity problem for a graph-based ML model, we propose adimension reduction algorithm based on the distance between molecular graphs,which can be used to examine the diversity of the molecular data set. Itprovides a promising way to build a minimum training set to improve predictionfor certain test sets where the space of molecular structures is predetermined.

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery