Programmable synthesis of high-entropy alloy electrocatalysts

High-entropy alloy (HEA) electrocatalysts have rapidly evolved from proof-of-concept demonstrations to application-oriented synthesis. However, early efforts largely relied on empirical strategies with limited structural control. Recently, attention has shifted toward programmable synthesis for targeted electrocatalytic reactions, where key variables, including composition, precursor release sequence, nucleation and growth pathways, and reaction kinetics, are deliberately regulated to achieve deterministic architectures. This transition is enabled by an improved understanding of HEA formation mechanisms across diverse synthetic routes, allowing precise control over structures from macroscopic morphology to atomic-level configurations. Establishing clear structure-performance relationships is therefore central to programmable HEA design. Such relationships can be constructed through the integration of high-throughput fabrication, advanced characterization, and computational simulations, collectively enabling the development of comprehensive structure-property databases. Machine learning further accelerates the identification of optimal configurations within the vast multicomponent design space. This review summarizes representative programmable synthesis strategies, systematically examines multiscale structure-performance relationships, and highlights key challenges in developing next-generation HEA electrocatalysts. High-entropy alloys (HEAs) have gained attention as promising electrocatalysts due to their tunability and structural stability. This review addresses this by proposing a programmable synthesis strategy that links synthesis parameters with structural evolution and catalytic performance. The authors discuss various synthesis methods, including Joule heating and template-based approaches, highlighting their advantages and limitations. They emphasize the importance of controlling HEA morphology and atomic-scale structures to enhance catalytic activity. Key findings include the role of atomic-scale heterogeneity in expanding adsorption energy ranges and the potential of data-driven approaches to optimize HEA compositions. The review suggests that future research should focus on creating open-access databases with detailed structural descriptors to guide the design of high-performance HEA electrocatalysts. This could lead to significant advancements in electrochemical reactions such as oxygen reduction and CO2 reduction. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author. This mini-review highlights programmable strategies for engineering the morphology and atomic-scale structures of high-entropy alloy (HEA) electrocatalysts. Template-directed synthesis enables the construction of tailored 0D–3D architectures, while atomic-level regulation optimizes local coordination environments and adsorption-energy landscapes. By integrating high-throughput synthesis, advanced characterization, computational modeling, and machine learning, structure–performance relationships can be established to accelerate the rational design of high-activity HEA electrocatalysts.