High-Entropy Design in Battery Materials for High Performance Electrochemical Energy Storage

Abstract

The growing demand for advanced electrochemical energy storage devices highlights challenges in battery materials, such as limited storage sites, slow ion/electron transport, and structural instability, which collectively impede improvements in energy density, rate performance, cycle life, and battery safety. To address these challenges, high-entropy design—a strategy integrating multiple elements through doping, compositional gradients, or alloying—has emerged as a transformative approach to simultaneously enhance thermodynamic stability and unlock synergistic “cocktail effects” in battery materials. By strategically combining elements with tailored atomic-scale interactions, such systems can achieve unprecedented performance between structural robustness and electrochemical activity. However, the design principles and synergistic effects within high-entropy materials (cathodes, electrolytes, anodes) remain poorly understood, complicated by their vast compositional and structural possibilities. In this review, we present a systematic analysis of how high-entropy strategies optimize material properties across three interdependent dimensions: (1) structural engineering (e.g., surface/interface engineering), (2) physical effects (e.g., lattice strain and size mismatch), and (3) electronic/chemical interactions (e.g., valence state modulation and electron delocalization). While entropy alone does not guarantee superior performance, we highlight that rational element selection and configuration design are critical to activating these mechanisms. Importantly, AI-driven framework integrating machine learning with first-principles modeling, can enable data-guided material discovery to decode the complexity of high-entropy systems. This framework systematically deciphers design principles, predicts performance trade-offs, and accelerates the translation of high-entropy materials into practical energy storage solutions.

Keywords

battery materials / design principle / high entropy design / machine learning

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