Work

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

Year: 2026

Type: article

Abstract: °, 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.

Source: Chemical Science

DOI: 10.1039/d6sc05970h

Authors: Dat M. Doan, Qilei Song, Anthony Kucernak, Kim E. Jelfs

Institution: Imperial College London

Language: English

Topic: Aqueous and Flow Batteries

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Imperial College London, Engineering and Physical Sciences Research Council

Awards: AI for Chemistry: AIchemy, G4591806611