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- W4225496776 abstract "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 data set 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 data set of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations." @default.
- W4225496776 created "2022-05-05" @default.
- W4225496776 creator A5007436273 @default.
- W4225496776 creator A5054748146 @default.
- W4225496776 creator A5064662152 @default.
- W4225496776 date "2022-01-07" @default.
- W4225496776 modified "2023-10-17" @default.
- W4225496776 title "Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models" @default.
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- W4225496776 doi "https://doi.org/10.1021/acs.jctc.1c01040" @default.
- W4225496776 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34991320" @default.
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