Proton–electron transfer (PET) reactions are rather common in chemistry and crucial in
energy storage applications. How electrons and protons are involved or which mechanism …

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 …

High-throughput computational screening (HTCS) is an effective tool to accelerate the
discovery of active materials for Li-ion batteries. For the evaluation of organic cathode …

Molecular design of redox-active materials with higher solubility and greater redox potential
windows is instrumental in enhancing the performance of redox flow batteries Here we …

Abstract Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the
most appealing technologies for large-scale energy storage due to their electroactive …

Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe
route to large-scale energy storage. The energy density is one of the key performance …

Redox-flow batteries are relevant to store energy from intermittent sources such as solar
panels or wind turbines, thereby smoothing their energy supply. Up to now, most redox-flow …

The redox properties of quinones underlie their unique characteristics as organic battery
components that outperform the conventional inorganic ones. Furthermore, these redox …

Identifying interesting redox‐active couples from the vastness of organic chemical space
requires rapid screening techniques. A good initial indicator for couples worthy of further …

In this study, we utilize a density functional theory-machine learning framework to develop a
high-throughput screening method for designing new molecular electrode materials. For this …