理论计算在高熵陶瓷领域的研究进展

doi:10.15541/jim20250342

摘要/Abstract

摘要：

高熵陶瓷(High-entropy ceramic, HEC)凭借其高熵效应、晶格畸变效应、迟滞扩散效应、鸡尾酒效应, 展现出优异的热学性能、力学性能和化学稳定性, 在航空航天、能源、核工业等领域具有巨大的应用潜力。然而, 由于HEC巨大的成分与结构空间, 传统试错法存在周期长、成本高等问题, 难以有效开展针对复杂体系的研究。因此, 理论计算成为破解这一难题的核心工具。为梳理近年来理论计算在HEC领域的研究进展, 本文聚焦当前主流的理论计算方法, 包括第一性原理计算、分子动力学模拟、机器学习以及相图计算技术等, 并结合高通量计算与性能描述符等研究范式, 全面论述这些方法在HEC研究中的关键作用与具体应用。本文首先简要概述HEC的基本特性和核心效应, 接着重点剖析上述计算方法的理论基础, 并详细阐述其在HEC组分设计、性能预测、微观结构解析与相稳定性评估等方面的应用。最后, 总结理论计算在研究多组元体系时面临的主要挑战, 如高质量数据集稀缺、构效关系模糊等, 并对该领域在数据驱动设计、跨尺度关联、极端环境模拟等方向的发展进行了前瞻性展望。

关键词: 高熵陶瓷, 理论计算, 第一性原理, 多尺度模拟, 机器学习, 综述

Abstract:

High-entropy ceramic (HEC) demonstrates exceptional thermal and mechanical properties, along with outstanding chemical stability, which can be attributed to their high entropy, lattice distortion, sluggish diffusion, and cocktail effects. However, the expansive compositional and structural space associated with HEC renders traditional trial-and-error methods time-consuming, costly and inadequate for investigation of complex systems. Thus, theoretical calculation has become an indispensable tool for addressing these challenges. To outline recent advances in theoretical calculation for HEC, this article focuses on prevalent calculation methods, including first-principles calculations, molecular dynamics, machine learning, and calculation of phase diagrams. Additionally, it discusses research paradigms such as high-throughput computing and performance descriptors, providing a comprehensive overview of their key roles and specific applications in HEC. The article first outlines fundamental characteristics and core effects of HEC, then turns to critically examine theoretical basis of these calculation methods, elaborating on their applications through specific examples in composition design, property prediction, microstructural parsing, and phase stability assessment. Finally, this paper summarizes the major challenges encountered in theoretical calculations in the study of multi-component systems, such as the scarcity of high-quality datasets and the ambiguity of structure-property relationships. It concludes with a forward-looking outlook on the development directions in this field, including data-driven design, cross-scale correlation, and extreme environment simulation.

Key words: high-entropy ceramic, theoretical calculation, first principles, multiscale simulation, machine learning, review

中图分类号:

TB321

解陈一, 缪花明, 张蔚然, 刘荣军, 王衍飞, 李端. 理论计算在高熵陶瓷领域的研究进展[J]. 无机材料学报, 2026, 41(5): 545-560.

XIE Chenyi, MIAO Huaming, ZHANG Weiran, LIU Rongjun, WANG Yanfei, LI Duan. Research Progress on Theoretical Calculation in the Field of High-entropy Ceramics[J]. Journal of Inorganic Materials, 2026, 41(5): 545-560.

图/表 9

图1 HEC领域研究概况

Fig. 1 Overview of research in the field of HEC (a) Amount of publications of HEC in Web of Science database from 2017 to 2024; (b) Percentage of publications on various types of HEC in 2024; (c) Schematic diagrams of HEC with different crystal structures[26]; (d) Relationship between entropy and number of components, as well as definition of medium-entropy and high-entropy ceramics

图2 高熵材料的四大效应

Fig. 2 Four major effects of high-entropy materials

图3 相关理论计算方法的发展历程

Fig. 3 Development history of relevant theoretical calculation methods

表1 不同理论计算方法的机制、尺度适用性、优势、局限性及应用场景的对比

Table 1 Comparison of mechanisms, scale applicability, advantages, limitations, and application scenarios of different theoretical computing methods

Method	Mechanism	Spatial scale	Time scale	Advantage	Limitation	Application
FPC	Quantum mechanics	Atomic/electronic scale (angstrom)	Femtosecond to picosecond level	High accuracy without empirical parameters	High computational cost and limited system size	Electronic structures, interface mechanisms
MDS	Newtonian mechanics and force fields	Nanoscale	Picosecond to microsecond level	Simulation of kinetic processes	Highly dependent on accuracy of the potential function	Mechanical properties, diffusion behavior
MCS	Probability sampling	Atomic to macroscopic scale	Equilibrium state	Exploring equilibrium state processes	Inability to simulate kinetic processes	Phase equilibrium prediction, formation energy calculation
CALPHAD	Gibbs free energy	Macro system	Thermodynamic equilibrium state	Predicting phase diagrams	Calibration dependent on experimental data	Phase diagram calculation, component optimization
ML	Algorithm and data-driven	Multi-scale	Instantaneous prediction	High-throughput screening and resolving complex associations	Weak model interpretability	Performance prediction, component optimization

图4 相结构预测相关的研究工作[66,76,82]

Fig. 4 Researches on phase structure prediction[66,76,82] (a) EFA of 56 kinds of high-entropy ceramics calculated by FPC[66]; (b) Flowchart of the HEBs formation capability descriptor prediction model[76]; (c) Pseudo-binary phase diagrams for (Ti-Zr-Hf-Nb-Ta-Mo)-C and (Ti-Zr-Hf-Nb-Ta)-C[82]

图5 高熵陶瓷的结构表征与性能关联研究[83,85]

Fig. 5 Researches on structural characterization and performance correlation of high-entropy ceramics[83,85] (a) “Composition-structure-elastic properties” correlation heatmapping of (TiZrNbTaMo)C[83]; (b) Spatial schematic diagrams of high-entropy ceramic components with different carbon vacancy concentrations[85]

图6 机器学习驱动的高熵陶瓷物理性能模拟与预测方法研究[86,88]

Fig. 6 Researches on simulation and prediction methods of physical properties of high-entropy ceramics driven by machine learning[86,88] (a) Schematic of transferable machine-learning-potential-based MDS for HECs[86]; (b) Performance of five ML models[88]

图7 扩散行为与原子尺度机制相关的研究工作[93,96]

Fig. 7 Researches on diffusive behavior and atomic scale mechanisms[93,96] (a) Adsorption energy and diffusion barrier of high-entropy diborides and four single-component diborides[93]; (b) Simplified schematic diagrams of oxidation mechanism of (Mo0.2Nb0.2Ta0.2V0.2W0.2)Si2 and MoSi2[96]

图8 数据驱动辅助材料设计相关的研究工作[61,97 -99]

Fig. 8 Researches on data-driven assisted material design[61,97 -99] (a) Preprocessing of characterization parameters based on their importance relative to CTE[61]; (b) Ranking of features which have the greatest impact on hardness[97]; (c) ML-assisted design strategy for prediction of mechanical properties and descriptor-property correlation analysis of HENs[98]; (d) Comparison of mechanical properties of high-entropy ceramic coatings[99]

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