Machine Learning-Guided Approach for Studying Solvation Environments

Basdogan, Yasemin; Groenenboom, Mitchell C.; Henderson, Ethan; De, Sandip; Rempe, Susan B.; Keith, John A.

doi:10.1021/acs.jctc.9b00605

Title: Machine Learning-Guided Approach for Studying Solvation Environments

Journal Article · 06 December 2019 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.9b00605 · OSTI ID:1770792

Basdogan, Yasemin ^[1]; Groenenboom, Mitchell C. ^[1]; Henderson, Ethan ^[1]; De, Sandip ^[2];

^[3];

^[1]

Univ. of Pittsburgh, PA (United States)
Ecole Polytechnique Federale Lausanne (Switzerland)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, in this work, 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 learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear 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 2+, 1+, 1–, and 2–, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. 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 within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Center for Integrated Nanotechnologies (CINT)

Sponsoring Organization:: National Science Foundation (NSF); R. K. Mellon Foundation; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1770792

Report Number(s):: SAND--2021-2565J; 694495

Journal Information:: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 1 Vol. 16; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Cluster chemistry
Free energy
Molecules
Solvation
Solvents

Title: Machine Learning-Guided Approach for Studying Solvation Environments

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