Machine Learning Analysis of the Thermodynamic Responses of In Situ Dielectric Spectroscopy Data in Amino Acids and Inorganic Electrolytes

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R ² > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R ² > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.