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 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.

Extension of the MST continuum solvation model to the RM1 semiempirical hamiltonian

2007 ◽  
Vol 29 (4) ◽  
pp. 578-587 ◽  
Author(s):  
Flavio Forti ◽  
Xavier Barril ◽  
F. Javier Luque ◽  
Modesto Orozco
Keyword(s):  
Continuum Solvation ◽  
Solvation Model ◽  
Continuum Solvation Model
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