Overview:recent studies of machine learning in phase prediction of high entropy alloys

作     者：Yong-Gang Yan Dan Lu Kun Wang 

作者机构：Alfred UniversityAlfredNY 14802USA 

出 版 物：《Tungsten》 (钨科技（英文）)

年 卷 期：2023年第5卷第1期

页      面：32-49页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Faculty Startup Fund in the New York State College of Ceramics at Alfred University。 

主　　题：Machine learning High entropy alloys Phase prediction 

摘      要：High entropy alloys(HEAs),especially refractory HEAs,have become a subject of interest in the past years due to their exceptional properties in terms of high-temperature strength,corrosion resistance,radiation tolerance,etc.under extreme environments.While the phase formation of these alloys significantly affects their properties.If the phase of HEAs can be forecasted before the experiments,the material design process can be greatly accelerated.The phase formation study of HEAs mainly relied on trial-and-error experiments and multi-scale computational simulations such as calculation of phase diagrams(CALPHAD) and density functional theory(DFT).However,those methods require massive time,man-power,and resources.As a highly efficient tool,machine learning(ML) method has been developed and applied to predict the phase formation of HEAs very recently.This review provided a comprehensive overview and analysis of the most recent research work in this area.First,we introduce ML methodologies applied in HEAs’ phase prediction in terms of principles,database,algorithm,and validation.We then summarize recent applications of the ML method in the phase prediction of HEAs.In the end,we propose possible solutions to the current problems and future research pathways for various challenges in the phase prediction of HEAs using ML.