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

## TL;DR

This paper introduces a machine learning model that accurately predicts reduction potentials of molecules across various solvents, enabling the design of new materials for applications like batteries.

## Contribution

The novel contribution is a graph neural network that generalizes to unseen solvents and enables inverse design of redox-active molecules.

## Key findings

- The model achieves a mean absolute error of approximately 0.2 eV in predicting reduction potentials.
- The model generalizes well to previously unseen solvents, a key limitation in prior methods.
- An evolutionary algorithm is used to design new molecules with desired redox properties for battery applications.

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