Accelerating the Discovery of Efficient High-Entropy Alloy Electrocatalysts: High-Throughput Experimentation and Data-Driven Strategies

High-entropy alloys (HEAs) present both significant potential and challenges for developing efficient electrocatalysts due to their diverse combinations and compositions. Here, we propose a procedural approach that combines high-throughput experimentation with data-driven strategies to accelerate the discovery of efficient HEA electrocatalysts for the hydrogen evolution reaction (HER). This enables the rapid preparation of HEA arrays with various element combinations and composition ratios within a model system. The intrinsic activity of the HEA arrays is swiftly screened using scanning electrochemical cell microscopy (SECCM), providing precise composition-activity data sets for the HEA system. An ensemble machine learning (EML) model is then used to predict the activity database for the composition subspace of the system. Based on these database results, two groups of promising catalysts are recommended and validated through actual electrocatalytic evaluations. This procedural approach, which combines high-throughput experimentation with data-driven strategies, provides a new pathway to accelerate the discovery of efficient HEA electrocatalysts.