High-Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next-Generation Synthesis and Applications

High-entropy materials (HEMs) with promising energy storage and conversion properties have recently attracted worldwide increasing research interest. Nevertheless, most research on the synthesis of HEMs focuses on a "trial and error" method without any guidance, which is very laborious and time-consuming. This review aims to provide an instructive approach to searching and developing new high-entropy energy materials in a much more efficient way. Toward materials design for future technologies, a fundamental understanding of the process/structure/property/performance linkage on an atomistic level will promote prescreening and selection of material candidates. With the help of computational material science, in which the fast development of computational capabilities that have a rapidly growing impact on new materials design, this fundamental understanding can be approached. Furthermore, high-throughput experimental methods, enabled by the advances in instrumentation and electronics, will accelerate the production of large quantities of results and stimulate the identification of the target products, adding knowledge in computational design. This review shows that combining computational preselection and verification by high-throughput can be an efficient approach to unveil the complexities of HEMs and design novel HEMs with enhanced properties for energy-related applications.