高熵陶瓷的功能性应用和数据驱动设计  

Functional Applications and Data-driven Design of High-entropy Ceramics

作　　者：张勇[1,2] 朱祥涵[2] ZHANG Yong;ZHU Xianghan(Key Laboratory of Silicon-based Materials for the Ministry of Education,School of Materials Science and Engineering,Fuyao University of Science and Technology,Fuzhou 350109,Fujian;State Key Laboratory for Advanced Metals and Materials,School of Materials Science and Engineering,University of Science and Technology Beijing,Beijing 100083)

机构地区：[1]福耀科技大学材料科学与工程学院硅基材料教育部重点实验室,福建福州350109 [2]北京科技大学材料科学与工程学院新金属材料全国重点实验室,北京100083

出　　处：《四川师范大学学报(自然科学版)》2025年第3期285-311,F0002,共28页Journal of Sichuan Normal University(Natural Science)

基　　金：中国创新群体基金(51921001)。

摘　　要：高熵陶瓷(HECs)作为高熵材料庞大家族的一员,其被定义为含有5种或更多阳离子或阴离子亚晶格、具有高构型熵的固溶体.HECs与高熵合金有着相似的“四大效应”,即高熵效应、晶格畸变效应、迟滞扩散效应以及协同增效作用.由于在成分和结构上的复杂性,高熵陶瓷一方面展现出了多样化的性能特点,在众多技术领域具有潜在应用价值,应用领域包括耐磨耐腐蚀涂层、热障涂层、吸波涂层、太阳能吸收和耐辐照涂层等.另一方面,巨大的成分空间使得实验试错法耗费的时间和成本不可忽视.在材料科学领域,借助机器学习的方法,通过数据驱动和高通量方法可以加速发现和识别新成分,实现新材料的相预测和性能预测.本文从高熵陶瓷的功能性应用出发,综述高熵陶瓷领域的数据驱动方法和高通量策略,旨在推动高熵陶瓷在功能性应用领域的发展和创新.High-entropy ceramics (HECs), as a member of the large family of high-entropy materials (HEMs), are defined as solid solutions containing five or more cationic or anionic sublattices with high configurational entropy. HECs and high-entropy alloys (HEAs) share the similar “four major effects”, including the high-entropy effect, the lattice distortion effect, the hysteresis-diffusion effect, and the synergistic effect. The compositional and structural complexity of HECs allows them to exhibit a diverse range of performance characteristics, which have the potential to be applied in numerous technological fields. These include, but are not limited to, wear and corrosion-resistant coatings, thermal barrier coatings, wave-absorbing coatings, solar energy-absorbing and irradiation-resistant coatings, and so on. Nevertheless, the expansive compositional space necessitates the time-consuming and costly experimental trial-and-error method as a significant factor in the development of new HECs. In the field of materials science, the discovery and identification of new compositions can be accelerated by data-driven and high-throughput methods that employ machine learning (ML) methods for phase prediction and property prediction of new materials. This paper presents a review of the functional applications of high-entropy ceramics, with a particular focus on data-driven methods and high-throughput strategies. The objective is to provide insights that can facilitate the advancement and innovation of high-entropy ceramics in functional applications.

关 键 词：高熵陶瓷 功能性应用 数据驱动 高通量策略 机器学习 

分 类 号：TG131[一般工业技术—材料科学与工程]