Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space.

ABSTRACT: Reduction potentials of redox-active molecules and materials are essential descriptors of their performance as catalysts, antioxidants, electrode materials, etc. For a given species, its practical applications often span a range of solvent environments, which profoundly impact its redox properties. In this work, we present a message passing graph neural network architecture with a Set Transformer readout trained on ca. 20,000 reduction potentials of chemically diverse closed- and open-shell organic redox-active molecules (the "ReSolved" data set), computed using a rigorously benchmarked density functional theory procedure. The predictor model affords high accuracy with mean absolute errors of ca. 0.2 eV and is uniquely able to generalize to previously unseen solvents. We coup

SUBMITTER: Fedorov R 

PROVIDER: S-EPMC12848968 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature