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

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 <i>ca</i>. 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 <i>ca</i>. 0.2 eV and is uniquely able to generalize to previously unseen solvents. We couple this architecture with an evolutionary algorithm to inverse-design synthetically accessible candidate molecules with target reduction potentials for several battery-related practical applications.