Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: A Case Study in the Quaternary System Ni-Pd-Pt-Ru for the Oxygen Evolution Reaction

TL;DR

This paper demonstrates how Bayesian optimization combined with high-throughput synthesis and characterization accelerates the discovery of optimal quaternary electrocatalyst compositions for oxygen evolution, reducing exploration time significantly.

Contribution

It introduces a combined approach of combinatorial sputtering, high-throughput testing, and Bayesian optimization for efficient exploration of complex multi-metal catalyst systems.

Findings

Identified the global activity maximum with less than 20% of the composition space explored.

Validated the activity trend with six additional material libraries.

Provided guidelines for efficient exploration of composition spaces using this method.

Abstract

The discovery of high-performance electrocatalysts is crucial for advancing sustainable energy technologies. Compositionally complex solid solutions comprising multiple metals offer promising catalytic properties, yet their exploration is challenging due to the combinatorial explosion of possible compositions. In this work, we combine combinatorial sputtering of thin-film materials libraries and their high-throughput characterization with Bayesian optimization to efficiently explore the quaternary composition space Ni-Pd-Pt-Ru for the oxygen evolution reaction in alkaline media. Using this method, the global activity optimum of pure Ru was identified after covering less than 20% of the complete composition space with six materials libraries. Six additional libraries were fabricated to validate the activity trend. The resulting dataset is used to formulate general guidelines for the efficient composition space exploration using combinatorial synthesis paired with Bayesian optimization.