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

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.