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

ABSTRACT: 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 potent

SUBMITTER: Ghule S 

PROVIDER: S-EPMC9017108 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature