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：0710[理学-生物学] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0812[工学-计算机科学与技术（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 calculations.Machine learning(ML)methods might address this challenge,but ML of HEAs has been hindered by the scarcity of HEA property data.In 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 calculated.The elastic property dataset was used to train a ML model with the Deep Sets architecture.The Deep Sets model has better predictive performance and generalizability compared to other ML models.Association 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.