In the past few years, there have been several computational high-throughput studies that combined quantum mechanical density functional theory (DFT) and machine learning (ML) to find potential candidates for flow batteries. Mostly, for sets of limited isomeric backbones, these studies primarily focused on solubility and redox potential of the hydrogen atom transfer reaction at pH = 0 when functional groups were decorated. ChemAxon software 44 has also developed powerful models for predicting pK a and solubility. However, its accuracy always depends on both the quality and the size of the dataset. To the best of our knowledge, the PET reaction mechanism has not yet been studied in a systematic high-throughput manner. This contribution attempts to carefully map the chemical and structural characteristics of a library of quinone-type compounds to their square-scheme representations of the potential for ET and PT. In this report, we provide (i) a deeper understanding of molecule design for energy storage applications from a screening of large chemical space and (ii) a trained ML model to predict so far unknown redox-active molecules.