Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model

Amin Alibakhshi () and Bernd Hartke
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Amin Alibakhshi: Christian-Albrechts-University
Bernd Hartke: Christian-Albrechts-University

Nature Communications, 2021, vol. 12, issue 1, 1-7

Abstract: Abstract Theoretical estimation of solvation free energy by continuum solvation models, as a standard approach in computational chemistry, is extensively applied by a broad range of scientific disciplines. Nevertheless, the current widely accepted solvation models are either inaccurate in reproducing experimentally determined solvation free energies or require a number of macroscopic observables which are not always readily available. In the present study, we develop and introduce the Machine-Learning Polarizable Continuum solvation Model (ML-PCM) for a substantial improvement of the predictability of solvation free energy. The performance and reliability of the developed models are validated through a rigorous and demanding validation procedure. The ML-PCM models developed in the present study improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude with almost no additional computational costs. A freely available software is developed and provided for a straightforward implementation of the new approach.

Date: 2021
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DOI: 10.1038/s41467-021-23724-6

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