scholarly journals Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models

2022 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Implicit And Explicit

scholarly journals Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

2021 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Density Functional ◽  
Implicit Solvent ◽  
Systematic Bias ◽  
Redox Potentials ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Implicit And Explicit

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

scholarly journals Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

2021 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Density Functional ◽  
Implicit Solvent ◽  
Systematic Bias ◽  
Redox Potentials ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Implicit And Explicit

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

scholarly journals Comparison of Implicit and Explicit Solvent Models for the Calculation of Solvation Free Energy in Organic Solvents

2017 ◽  
Vol 13 (3) ◽  
pp. 1034-1043 ◽  
Author(s):  
Jin Zhang ◽  
Haiyang Zhang ◽  
Tao Wu ◽  
Qi Wang ◽  
David van der Spoel
Keyword(s):  
Free Energy ◽  
Organic Solvents ◽  
Solvation Free Energy ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Implicit And Explicit
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