Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation

TL;DR

This paper presents a data-driven discovery cycle combining computational modeling and high-throughput experimentation to identify optimal complex solid solution electrocatalysts for electrochemical reactions, exemplified by oxygen reduction.

Contribution

It introduces a novel iterative approach integrating prediction and experimentation to efficiently discover high-performance solid solution electrocatalysts.

Findings

Refined models accurately predict activity maxima.

The method successfully identifies optimal complex solid solutions.

High-throughput data enhances model accuracy.

Abstract

Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datasets are then input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru for the oxygen reduction reaction. The method can identify optimal complex solid solutions for electrochemical reactions in an unprecedented manner.