Composition design of high-entropy alloys with deep sets learning

作     者：Jie Zhang Chen Cai George Kim Yusu Wang Wei Chen 

作者机构：Department of MechanicalMaterialsand Aerospace EngineeringIllinois Institute of TechnologyChicagoIL60616USA Halıcıoğlu Data Science InstituteUniversity of California San DiegoLa JollaCA92093USA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2022年第8卷第1期

页      面：834-844页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：This material is based upon work supported by the National Science Foundation under Grant Nos.OAC-1940114,OAC-2039794,and DMR-1945380 This work used the Extreme Science and Engineering Discovery Environment(XSEDE),which is supported by National Science Foundation grant number ACI-1548562 This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231 

主　　题：alloys alloy entropy 

摘      要：High entropy alloys(HEAs)are an important material class in the development of next-generation structural materials,but the astronomically large composition space cannot be efficiently explored by experiments or first-principles *** learning(ML)methods might address this challenge,but ML of HEAs has been hindered by the scarcity of HEA property *** this work,the EMTO-CPA method was used to generate a large HEA dataset(spanning a composition space of 14 elements)containing 7086 cubic HEA structures with structural properties,1911 of which have the complete elastic tensor *** elastic property dataset was used to train a ML model with the Deep Sets *** Deep Sets model has better predictive performance and generalizability compared to other ML *** rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.