Bayesian optimization with active learning of design constraints using an entropy-based approach  

作　　者：Danial Khatamsaz Brent Vela Prashant Singh Duane D.Johnson Douglas Allaire Raymundo Arróyave 

机构地区：[1]J.Mike Walker’66 Department of Mechanical Engineering,Texas A&M University,College Station,TX 77843,USA [2]Department of Materials Science and Engineering,Texas A&M University,College Station,TX 77843,USA [3]Ames Laboratory,U.S.Department of Energy,Iowa State University,Ames,IA 50011,USA [4]Department of Materials Science&Engineering,Iowa State University,Ames,IA 50011,USA [5]Department of Industrial and Systems Engineering,Texas A&M University,College Station,TX 77843,USA

出　　处：《npj Computational Materials》2023年第1期1866-1879,共14页计算材料学（英文）

基　　金：The authors acknowledge the support from the U.S.Department of Energy(DOE)ARPA-E ULTIMATE Program through Project DE-AR0001427 and DEVCOM-ARL under Contract No.W911NF2220106(HTMDEC);B.V.acknowledges the support of NSF through Grant No.DGE-1545403;D.K.acknowledges the support of NSF through Grant No.CDSE-2001333;R.A.acknowledges the support from Grants No.NSF-CISE-1835690 and NSF-DMREF-2119103.High-throughput CALPHAD and DFT calculations were carried out partly at the Texas A&M High-Performance Research Computing(HPRC)Facility.ARPA-E supported the applications of theory in this work.In contrast,the theory development(KKR-CPA and SCRAPs by DDJ/PS)at Ames National Laboratory were supported by the U.S.DOE,Office of Science,Basic Energy Sciences,Materials Science and Engineering Department.Ames Laboratory is operated by Iowa State University for the U.S.DOE under contract DE-AC02-07CH11358.

摘　　要：The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints.Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures,as well as possess low density,high thermal conductivity,narrow solidification range,high solidus temperature,and a small linear thermal expansion coefficient.Traditional Integrated Computational Materials Engineering(ICME)methods are not sufficient for exploring combinatorially-vast alloy design spaces,optimizing for multiple objectives,nor ensuring that multiple constraints are met.In this work,we propose an approach for solving a constrained multi-objective materials design problem over a large composition space,specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy(MPEA)for potential use in next-generation gas turbine blades.Our approach is able to learn and adapt to unknown constraints in the design space,making decisions about the best course of action at each stage of the process.As a result,we identify 21 Pareto-optimal alloys that satisfy all constraints.Our proposed framework is significantly more efficient and faster than a brute force approach.

关 键 词：ALLOYS SOLIDIFICATION ALLOY 

分 类 号：TG13[一般工业技术—材料科学与工程]