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.
Basdogan, Yasemin, et al. "Machine Learning-Guided Approach for Studying Solvation Environments." Journal of Chemical Theory and Computation, vol. 16, no. 1, Dec. 2019. https://doi.org/10.1021/acs.jctc.9b00605
Basdogan, Yasemin, Groenenboom, Mitchell C., Henderson, Ethan, De, Sandip, Rempe, Susan B., & Keith, John A. (2019). Machine Learning-Guided Approach for Studying Solvation Environments. Journal of Chemical Theory and Computation, 16(1). https://doi.org/10.1021/acs.jctc.9b00605
Basdogan, Yasemin, Groenenboom, Mitchell C., Henderson, Ethan, et al., "Machine Learning-Guided Approach for Studying Solvation Environments," Journal of Chemical Theory and Computation 16, no. 1 (2019), https://doi.org/10.1021/acs.jctc.9b00605
@article{osti_1770792,
author = {Basdogan, Yasemin and Groenenboom, Mitchell C. and Henderson, Ethan and De, Sandip and Rempe, Susan B. and Keith, John A.},
title = {Machine Learning-Guided Approach for Studying Solvation Environments},
annote = {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.},
doi = {10.1021/acs.jctc.9b00605},
url = {https://www.osti.gov/biblio/1770792},
journal = {Journal of Chemical Theory and Computation},
issn = {ISSN 1549-9618},
number = {1},
volume = {16},
place = {United States},
publisher = {American Chemical Society},
year = {2019},
month = {12}}
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