continuum solvation model
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2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke

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.



2021 ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke

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>



2021 ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke

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>



2018 ◽  
Vol 54 (74) ◽  
pp. 10431-10434
Author(s):  
Nicolas Sieffert ◽  
Amol Thakkar ◽  
Michael Bühl

We developed a computationally-efficient protocol based on Density Functional Theory (DFT) and a continuum solvation model (CSM) to predict reaction free energies of complexation reactions of uranyl in liquid ammonia.





2014 ◽  
Vol 126 ◽  
pp. 112-121 ◽  
Author(s):  
Sanjukta Bhoi ◽  
Debanjan Dey ◽  
Tamal Banerjee ◽  
Kaustubha Mohanty


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