高熵合金强韧化理论建模与模拟研究进展

高熵合金因其优异的性能受到广泛关注，如高强度、高硬度、高韧性、高耐磨、高耐辐照、高耐腐蚀、高电阻、高耐热等，有望应用于核能、航天航空等重要领域和重大装备。从高熵合金制备、组织结构以及性能表征等方面开展的实验研究表明其独特的性质依赖于高熵合金高熵效应、晶格畸变和扩散迟滞。在微观尺度以及宏观尺度, 理论模型和数值模拟为研究高熵合金微观机理和力学特性提供了一种方法。建立从高熵合金的微观结构与变形机理到宏观独特力学性能的联系是一个多尺度的科学问题。最近，基于实验观察结果，采用多尺度的理论与模拟方法（第一性原理、分子动力学、离散位错动力学、晶体塑性有限元、微结构依赖的理论模型），研究了高熵合金层错能、弹性模量、扩散系数以及相稳定性，揭示了高熵合金变形与强韧化机制。本文综述多尺度计算在高熵合金力学性能和变形行为方面的研究进展，并对高熵合金在原位变形实验、高通量技术以及机器学习方面的研究进行简要展望。

Progress in theoretical modeling and simulation on strengthening and toughening of high entropy alloys

High entropy alloys (HEAs) have attracted extensive attention due to their excellent properties, such as high strength, high hardness, high toughness, high wear resistance, high radiation resistance, high corrosion resistance, high resistance, high heat resistance, which are expected to be used in important fields such as nuclear energy and aerospace, as well as major equipment. The experimental studies on the preparation, microstructure and properties of HEAs show that their unique properties depend on the high entropy effect, lattice distortion and diffusion hysteresis. At the micro-scale and macro-scale, theoretical models and numerical simulations provide a method for studying the micro mechanism and mechanical properties of HEAs. Establishing the connection from the microstructure and deformation mechanism of HEAs to the unique macroscopic mechanical properties is a multi-scale scientific problem. Recently, based on experimental observation, using multi-scale theory and simulation (first principle method, molecular dynamics, discrete dislocation dynamics, crystal plastic finite element, microstructure dependent theoretical model), the stacking energy, elastic modulus, diffusion coefficient and phase stability of HEAs are studied, and then the deformation and strengthening mechanisms of HEAs are revealed. In this paper, the research progress of multi-scale calculation on mechanical properties and deformation behavior of HEAs is reviewed, and in situ deformation experiment, high-throughput technology and machine learning in HEA is briefly prospected.