High‐entropy oxides (HEOs) have garnered significant attention within the realm of rechargeable batteries owing to their distinctive advantages, which encompass diverse structural attributes, customizable compositions, entropy‐driven stabilization effects, and remarkable superionic conductivity. Despite the brilliance of HEOs in energy conversion and storage applications, there is still lacking a comprehensive review for both entry‐level and experienced researchers, which succinctly encapsulates the present status and the challenges inherent to HEOs, spanning structural features, intrinsic properties, prevalent synthetic methodologies, and diversified applications in rechargeable batteries. Within this review, the endeavor is to distill the structural characteristics, ionic conductivity, and entropy stabilization effects, explore the practical applications of HEOs in the realm of rechargeable batteries (lithium‐ion, sodium‐ion, and lithium‐sulfur batteries), including anode and cathode materials, electrolytes, and electrocatalysts. The review seeks to furnish an overview of the evolving landscape of HEOs‐based cell component materials, shedding light on the progress made and the hurdles encountered, as well as serving as the guidance for HEOs compositions design and optimization strategy to enhance the reversible structural stability, electrical properties, and electrochemical performance of rechargeable batteries in the realm of energy storage and conversion.

High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HEAs, the accuracy of prediction is limited owing to the indirect correlation between the theoretical calculation values and performances. Recently, machine learning (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design. The advantages and limitations of these ML models and algorithms are analyzed and their potential weaknesses and corresponding optimization strategies are discussed as well. This review suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future HEA design.

Along with the globalization of environmental problems and the rapid development of the field of nuclear technologies, the severe irradiation damage of materials has become a big issue, restricting the development of advanced nuclear reactor systems. Refractory high-entropy alloys (RHEAs) have the characteristics of a complex composition, a short-range order, and lattice distortion and possess a high phase stability, outstanding mechanical properties, and excellent irradiation resistance at elevated temperatures; thus, they are expected to be promising candidates for advanced nuclear reactors. This review summarizes the design, preparation, and irradiation resistance of irradiation-tolerant RHEAs. It encompasses a comprehensive analysis of various aspects, including the evolution of defects, changes in microstructure, and the degradation in properties. Furthermore, the challenges and insufficiently researched areas regarding these alloys are identified and discussed. Building on this foundation, the review also provides a forward-looking perspective, outlining potential avenues for future research.

High-entropy alloys have demonstrated significant application potential in many industrial fields due to their outstanding comprehensive properties. However, the complex multi-component compositions pose challenges for traditional design approaches. In recent years, artificial intelligence (AI) technology, with its powerful capabilities in data analysis, prediction, and optimization, has provided new pathways for rapid discovery and performance modulation of high-entropy alloys. This paper systematically reviews the latest advancements in AI applications for high-entropy alloy design, covering key technologies such as machine learning models (e.g., active learning, generative models, transfer learning), high-throughput computing and experimental data processing, phase structure and property prediction. It also presents typical application cases, including compositional optimization, phase structure prediction, performance synergistic regulation, and novel material discovery. Although AI has significantly improved design efficiency and accuracy, challenges remain, such as the scarcity of high-quality data, insufficient model interpretability, and interdisciplinary integration. Future efforts should focus on building a more robust data ecosystem, enhancing model transparency, and strengthening closed-loop validation between AI and experimental science to advance intelligent design and engineering applications of high-entropy alloys.