Key Takeaways & Executive Findings
- •• High-entropy materials (HEMs) offer unique advantages for batteries, including high configurational entropy, lattice distortion, and synergistic cocktail effects, which enhance structural stability, electronic conductivity, and ionic transport. • The review proposes a multidimensional design paradigm that integrates synergistic mechanisms across cathodes, anodes, electrolytes, and electrocatalysts, addressing fragmented knowledge in structure–property relationships. • Entropy-mediated structural tailoring improves cycle stability and ionic conductivity in lithium, sodium, and potassium-ion batteries, with high-entropy effects stabilizing solid-electrolyte interphases and suppressing transition metal dissolution. • Machine learning-driven composition screening and sustainable manufacturing present emerging opportunities, while performance variability and cost-benefit analysis remain critical challenges for industrial implementation.
Abstract
High-entropy materials (HEMs), an innovative class of materials with complex stoichiometry, have recently garnered considerable attention in energy storage applications. While their multi-element compositions (five or more principal elements in nearly equiatomic proportions) confer unique advantages such as high configurational entropy, lattice distortion, and synergistic cocktail effects, the fundamental understanding of structure–property relationships in battery systems remains fragmented across existing studies. This review addresses critical research gaps by proposing a multidimensional design paradigm that systematically integrates synergistic mechanisms spanning cathodes, anodes, electrolytes, and electrocatalysts. We provide an in-depth analysis of HEMs’ thermodynamic/kinetic stabilization principles and structure-regulated electrochemical properties, integrating and establishing quantitative correlations between entropy-driven phase stability and charge transport dynamics. By summarizing the performance benchmarking results of lithium/sodium/potassium-ion battery components, we reveal how entropy-mediated structural tailoring enhances cycle stability and ionic conductivity. Notably, we pioneer the systematic association of high-entropy effects to electrochemical interfaces, demonstrating their unique potential in stabilizing solid-electrolyte interphases and suppressing transition metal dissolution. Emerging opportunities in machine learning-driven composition screening and sustainable manufacturing are discussed alongside critical challenges, including performance variability metrics and cost-benefit analysis for industrial implementation. This work provides both fundamental insights and practical guidelines for advancing HEMs toward next-generation battery technologies.
1. Introduction
High-entropy materials (HEMs) represent an emerging class of materials [1–2] that have attracted substantial attention in recent years due to their unique properties [3–4] and potential applications across various fields, including energy storage [5–6], catalysis [7–9], and structural materials [10–11]. HEMs are defined by their composition of five or more elements in nearly equiatomic proportions (5at%–35at% each) [12]. This composition leads to a high configurational entropy, which is a key factor in determining the material’s properties [13].
The high-entropy effect is not the unique feature of these materials. Other effects, such as lattice distortion, sluggish diffusion, and the cocktail effect, also contribute to their exceptional properties [14]. These effects collectively enhance the material’s stability, conductivity, and electrochemical performance, making HEMs highly attractive for energy-storage applications [15–16].
The increasing demand for high-performance batteries has driven the search for new materials that can overcome the limitations of traditional battery components [17]. Traditional cathode materials, such as lithium cobalt oxide (LiCoO2) and lithium iron phosphate (LiFePO4), and anode materials, such as graphite, have been widely used in lithium-ion batteries (LIBs) [18]. However, these materials face several challenges, including capacity degradation, poor thermal stability, and insufficient cycle life [19]. Additionally, the growing need for sustainable and cost-effective energy storage solutions has highlighted the importance of developing materials that can provide higher energy densities, longer lifetimes, and improved safety [20].
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Jiasheng Wang, Jianzhong Jiang, Peter K. Liaw, Yong Zhang (2025). Properties and performances of high-entropy materials in batteries. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-025-3275-7
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Frequently Asked Questions
What are high-entropy materials (HEMs) and why are they important for batteries?
High-entropy materials (HEMs) are a class of materials composed of five or more principal elements in nearly equiatomic proportions. Their high configurational entropy, lattice distortion, and synergistic cocktail effects confer unique advantages such as enhanced structural stability, electronic conductivity, and ionic transport, making them promising candidates for next-generation battery technologies.
How do high-entropy effects improve battery performance?
High-entropy effects suppress phase transitions and element segregation during charge-discharge cycles, enhancing cycle stability and ionic conductivity. They also stabilize solid-electrolyte interphases and suppress transition metal dissolution, leading to improved battery longevity and safety.
What are the key challenges for industrial implementation of HEMs in batteries?
Critical challenges include performance variability metrics, cost-benefit analysis, and the need for sustainable manufacturing processes. Additionally, establishing universally accepted structure-property relationships remains a gap that requires further research.
What is the multidimensional design paradigm proposed in this review?
The review proposes a multidimensional design paradigm that systematically integrates synergistic mechanisms across cathodes, anodes, electrolytes, and electrocatalysts, aiming to provide a comprehensive understanding of HEMs in battery systems and guide future material design.
How can machine learning contribute to the development of HEMs for batteries?
Machine learning-driven composition screening can accelerate the discovery of optimal HEM compositions by predicting their properties and performance, thereby reducing experimental time and cost, and enabling more efficient design of high-performance battery materials.
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