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 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 couple this architecture with an evolutionary algorithm to inverse-design synthetically accessible candidate molecules with target reduction potentials for several battery-related practical applications.

pubs.acs.org

Fragment to framework: automatic fragmentation of covalent organic frameworks into building blocks for band gap analysis

Understanding structure–property relationships in ordered functional materials is essential for their rational design and optimisation. Fragment-based approaches relating materials’ properties to thos...

pubs.rsc.org