Machine Learning Guided Approach for Studying Solvation Environments

Molecular level understanding and characterization of solvation environments is often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics based cluster-continuum approach for calculating single ion solvation free energies. This approach uses a global optimization procedure to identify low energy molecular clusters with different numbers of explicit solvent molecules and then employs the Smooth Overlap for Atomic Positions (SOAP) machine learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch-maps, a non-linear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges of 2+, 1+, 1−, and 2−, we find that the solvation environment around ea...