These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Solvation_ChemRxiv.pdf (8.41 MB)

Machine Learning Guided Approach for Studying Solvation Environments

submitted on 19.06.2019, 02:30 and posted on 19.06.2019, 16:24 by Yasemin Basdogan, Mitchell C. Groenenboom, Ethan Henderson, Sandip De, Susan Rempe, John Keith

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) kernel to quantify the similarity between different low energy solute environments. From these data, we use sketch-map, a non-linear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies with errors within five percent of experimental measurements for most cases.





Email Address of Submitting Author


University of Pittsburgh



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest