RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning

Organic redox-active molecules (ORMs) are promising components of aqueous electrolytes in redox flow batteries (RFBs), a scalable electrochemical energy storage technology designed to address the intermittency of renewable energy. However, the development of aqueous ORMs is challenged by the need to satisfy multiple property requirements, including redox potential (E°), solubility and stability under operating conditions. Among these, redox potential is a critical parameter, as the thermodynamic stability window of water under standard conditions is limited to 0.000-1.229 V, making accurate prediction essential to designing new ORMs. Conventional computational approaches for estimating E°, such as density functional theory (DFT), can exhibit errors in the range of 0.2-0.5 V relative to experiment. Here, we present RedoxDB, a new database of >7500 DFT-calculated redox potentials for aqueous ORMs, which we use to pre-train a prediction model. Our prediction model, RedEx, is an ensemble graph attention network (GAT) employing transfer learning for the accurate prediction of experimental redox potential values, to close the domain gap between DFT and experiment. The model is subsequently fine-tuned with a curated dataset of experimental redox potential values taken from the literature. We demonstrate that this transfer learning strategy improves predictive performance, achieving a mean absolute error of 0.057 V, significantly reducing the domain gap between DFT and experiment. The attention mechanism of the GAT model also provides interpretability, connecting predictions directly to chemically important structural motifs. We envisage this model will help facilitate the design of novel redox couples for aqueous organic RFBs. The associated code is available at https://github.com/datmdoan/RedEx.