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高通量计算与机器学习驱动高熵合金的研究进展
Research progress in high-entropy alloys driven by high throughput computation and machine learning

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张聪 1,2   刘杰 2   解树一 2   徐斌 2   尹海清 1,2 *   刘斌斌 3   曲选辉 1,4  
文摘 高熵合金因其多种合金元素以等原子比或近等原子比的组合而具有高熵效应、严重的晶格畸变、缓慢扩散以及特殊而优异的材料性质等特点,在各个领域引起极大的关注。其高强度和硬度、抗疲劳性、优异的耐腐蚀性、耐辐照性以及接近零的热膨胀系数、催化响应、热电响应及光电转换等特性,使高熵合金在许多方面有潜在的应用。高通量计算及机器学习技术迅速成为探索高熵合金巨大成分空间和综合预测材料性能的有力手段。本文介绍高通量计算与机器学习的基本概念,论述第一性原理计算、热动力学计算与机器学习在高熵合金研究中的优势,并总结它们在高熵合金成分筛选、相与组织计算以及性能预测等方面的应用研究现状。最后提出该领域目前存在的问题,并提供解决思路与未来展望,包括开发适用于高熵合金的第一性原理计算与机器学习工具、构建高质量高熵合金数据库、将高通量计算与机器学习相融合对高熵合金的力学及服役性能进行全局优化等。
其他语种文摘 High-entropy alloys have attracted great attention in various fields due to their high-entropy effect,severe lattice distortion,slow diffusion and special and excellent material performance due to the combination of various alloying elements in equal or near-equal molar proportions.Its high strength and hardness,fatigue resistance,excellent corrosion resistance,radiation resistance,nearzero thermal expansion coefficient,catalytic response,thermoelectric response and photoelectric conversion make high-entropy alloys have potential applications in many aspects.High-throughput computation and machine learning technology have rapidly become powerful tools to explore the huge composition space of high-entropy alloys and comprehensively predict material properties.The basic concepts of high-throughput computing and machine learning were introduced in this paper as well as the advantages of first-principles calculation,thermodynamic/kinetic calculation and machine learning in the research of high-entropy alloys.The application research status of high-entropy alloy composition screening,phase and microstructure calculations and performance prediction were summarized.In the final part,the existing problems,and the solutions and future prospects of this field were summarized,including developing tools for first-principles calculations and machine learning of high-entropy alloys,building high-quality databases for high-entropy alloys and integrating highthroughput computing with machine learning to globally optimize the mechanical property and service performance of high-entropy alloys.
来源 材料工程 ,2023,51(3):1-16 【核心库】
DOI 10.11868/j.issn.1001-4381.2022.000997
关键词 高熵合金 ; 热力学 ; 第一性原理 ; 机器学习 ; 性能优化
地址

1. 北京科技大学, 北京材料基因工程高精尖创新中心, 北京, 100083  

2. 北京科技大学, 钢铁共性技术协同创新中心, 北京, 100083  

3. 北京科技大学, 新金属材料国家重点实验室, 北京, 100083  

4. 北京科技大学新材料技术研究院, 北京, 100083

语种 中文
文献类型 综述型
ISSN 1001-4381
学科 金属学与金属工艺
基金 国家自然科学基金叶企孙基金项目 ;  北京市自然科学基金
文献收藏号 CSCD:7445139

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引证文献 9 篇

1 田震 核结构材料用多主元合金辐照损伤的研究进展 材料工程,2024,52(1):1-15
CSCD被引 1 次

2 高田创 机器学习驱动难熔高熵合金设计的现状与展望 材料工程,2024,52(1):27-44
CSCD被引 2 次

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