Combinatorial materials discovery strategy for high entropy alloy electrocatalysts using deposition source permutations

TL;DR

This paper introduces a novel combinatorial strategy using deposition source permutations to efficiently explore the vast composition space of high entropy alloy electrocatalysts, leading to the discovery of highly active compositions for oxygen reduction.

Contribution

The study develops a permutation-based deposition approach to optimize composition sampling in high entropy alloys, enabling efficient identification of active electrocatalysts.

Findings

Identified a highly active alloy composition for oxygen reduction.

Demonstrated the effectiveness of source permutation strategy in exploring composition space.

Unsupervised machine learning revealed complex factors influencing electrochemical activity.

Abstract

High entropy alloys offer a huge search space for new electrocatalysts. Searching for a global property maximum in one quinary system could require, depending on compositional resolution, the synthesis of up to 10E6 samples which is impossible using conventional approaches. Co-sputtered materials libraries address this challenge by synthesis of controlled composition gradients of each element. However, even such a materials library covers less than 1% of the composition space of a quinary system. We present a new strategy using deposition source permutations optimized for highest improvement of the covered new compositions. Using this approach, the composition space can be sampled in different subsections allowing identification of the contribution of individual elements and their combinations on electrochemical activity. Unsupervised machine learning reveals that electrochemical activity is governed by the complex interplay of chemical and structural factors. Out of 2394 measured compositions, a new highly active composition for the oxygen reduction reaction around Ru17Rh5Pd19Ir29Pt30 was identified.