Work

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

Year: 2026

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide 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.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/acs.jcim.5c01450

Authors: Rostislav Fedorov, Anastasiia Nihei, Ganna Gryn’ova

Institutions: Heidelberg University, Heidelberg Institute for Theoretical Studies, University of Birmingham

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: Deutsche Forschungsgemeinschaft, Klaus Tschira Stiftung