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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Solvation Free Energy ◽  
Continuum Solvation ◽  
Free Energies ◽  
Solvation Model ◽  
Continuum Solvation Model ◽  
Solvation Models ◽  
Solvation Free Energies ◽  
Polarizable Continuum

AbstractTheoretical 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.

scholarly journals Improved Prediction of Solvation Free Energies by Machine-Learning Polarizable Continuum Solvation Model

2021 ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke
Keyword(s):  
Machine Learning ◽  
Substantial Improvement ◽  
Continuum Solvation ◽  
Solvation Model ◽  
Continuum Solvation Model ◽  
Order Of Magnitude ◽  
Validation Procedure ◽  
Solvation Models ◽  
Solvation Free Energies ◽  
Polarizable Continuum

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.<br>

scholarly journals Improved Prediction of Solvation Free Energies by Machine-Learning Polarizable Continuum Solvation Model

2021 ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke
Keyword(s):  
Machine Learning ◽  
Substantial Improvement ◽  
Continuum Solvation ◽  
Solvation Model ◽  
Continuum Solvation Model ◽  
Order Of Magnitude ◽  
Validation Procedure ◽  
Solvation Models ◽  
Solvation Free Energies ◽  
Polarizable Continuum

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.<br>

scholarly journals Assessment of the SM12, SM8, and SMD Solvation Models for Predicting Limiting Activity Coefficients at 298.15 K

2020 ◽  
Author(s):  
Sydnee N. Roese ◽  
Justin D. Heintz ◽  
Cole B. Uzat ◽  
Alexa J. Schmidt ◽  
Griffin Margulis ◽  
...  
Keyword(s):  
Free Energy ◽  
Phase Equilibrium ◽  
Activity Coefficients ◽  
Solvation Free Energy ◽  
Excess Gibbs Free Energy ◽  
Implicit Solvent ◽  
Solvent Models ◽  
Comparable Performance ◽  
Solvation Models ◽  
Solvation Free Energies

The SM<i>x</i> (<i>x</i>= 12, 8, or D) universal solvent models are implicit solvent models which using electronic structure calculations can compute solvation free energies at 298.15 K. While solvation free energy is an important thermophysical property, within the thermodynamic modeling of phase equilibrium, limiting (or infinite dilution) activity coefficients are preferred since they may be used to parameterize excess Gibbs free energy models to model phase equilibrium. Conveniently, the two quantities are related. Therefore the present study was performed to assess the ability to use the SM<i>x</i> universal solvent models to predict limiting activity coefficients. Two methods of calculating the limiting activity coefficient where compared: 1) The solvation free energy and self-solvation free energy were both predicted and 2) the self-solvation free energy was computed using readily available vapor pressure data. Overall the first method is preferred as it results in a cancellation of errors, specifically for the case in which water is a solute. The SM12 model was compared to both UNIFAC and MOSCED. MOSCED was the highest performer, yet had the smallest available compound inventory. UNIFAC and SM12 exhibited comparable performance. Therefore further exploration and research should be conducted into the viability of using the SM<i>x</i> models for phase equilibrium calculations.

Assessing the hydration free energy of a homologous series of polyols with classical and quantum mechanical solvation models

2014 ◽  
Vol 16 (33) ◽  
pp. 17863-17868 ◽  
Author(s):  
Thaciana Malaspina ◽  
Leonardo M. Abreu ◽  
Tertius L. Fonseca ◽  
Eudes Fileti
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Homologous Series ◽  
Continuum Model ◽  
Polarizable Continuum Model ◽  
Quantum Mechanical ◽  
Hydration Free Energy ◽  
Solvation Model ◽  
Solvation Models ◽  
Polarizable Continuum

Molecular dynamics (MD) and the polarizable continuum model (PCM) in combination with the SMD solvation model were used to study the hydration free energy of the homologous series of polyols, CnHn+2(OH)n (1 ≤ n ≤ 7).

scholarly journals Assessment of the SM12, SM8, and SMD Solvation Models for Predicting Limiting Activity Coefficients at 298.15 K

2020 ◽  
Author(s):  
Sydnee N. Roese ◽  
Justin D. Heintz ◽  
Cole B. Uzat ◽  
Alexa J. Schmidt ◽  
Griffin Margulis ◽  
...  
Keyword(s):  
Free Energy ◽  
Phase Equilibrium ◽  
Activity Coefficients ◽  
Solvation Free Energy ◽  
Excess Gibbs Free Energy ◽  
Implicit Solvent ◽  
Solvent Models ◽  
Comparable Performance ◽  
Solvation Models ◽  
Solvation Free Energies

The SM<i>x</i> (<i>x</i>= 12, 8, or D) universal solvent models are implicit solvent models which using electronic structure calculations can compute solvation free energies at 298.15 K. While solvation free energy is an important thermophysical property, within the thermodynamic modeling of phase equilibrium, limiting (or infinite dilution) activity coefficients are preferred since they may be used to parameterize excess Gibbs free energy models to model phase equilibrium. Conveniently, the two quantities are related. Therefore the present study was performed to assess the ability to use the SM<i>x</i> universal solvent models to predict limiting activity coefficients. Two methods of calculating the limiting activity coefficient where compared: 1) The solvation free energy and self-solvation free energy were both predicted and 2) the self-solvation free energy was computed using readily available vapor pressure data. Overall the first method is preferred as it results in a cancellation of errors, specifically for the case in which water is a solute. The SM12 model was compared to both UNIFAC and MOSCED. MOSCED was the highest performer, yet had the smallest available compound inventory. UNIFAC and SM12 exhibited comparable performance. Therefore further exploration and research should be conducted into the viability of using the SM<i>x</i> models for phase equilibrium calculations.

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