Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Khatamsaz, Danial; Vela, Brent; Singh, Prashant; Johnson, Duane D.; Allaire, Douglas; Arróyave, Raymundo

doi:10.1016/j.actamat.2022.118133

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Journal Article · Wed Jun 29 00:00:00 EDT 2022 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2022.118133· OSTI ID:1874802

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Texas A & M University at Galveston, TX (United States)
Texas A & M University at Galveston, TX (United States); Ames Lab., and Iowa State University, Ames, IA (United States)
Ames Lab., and Iowa State University, Ames, IA (United States)

We report Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and exploit materials design spaces. To date, most BO approaches to materials design have focused on the materials discovery problem as if it were a single expensive-to-query ‘black box’ in which the target is to optimize a single objective (i.e., material property or performance metric). Also, such approaches tend to be constraint agnostic. Here, we present a novel multi-information BO framework capable of actively learning materials design as a multiple objectives and constraints problem. We demonstrate this framework by optimally exploring a Refractory Multi-Principal-Element Alloy (MPEA) space, here specifically, the system Mo-Nb-Ti-V-W. The MPEAs are explored to optimize two density-functional theory (DFT) derived ductility indicators (Pugh’s Ratio and Cauchy pressure) while learning design constraints relevant to the manufacturing of high-temperature gas-turbine components. Alloys in the BO Pareto-front are analyzed using DFT to gain an insight into fundamental atomic and electronic underpinning for their superior performance, as evaluated within this framework.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States); Texas A & M Univ., College Station, TX (United States); Texas A&M University, College Station, TX (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-07CH11358; AR0001427

OSTI ID:: 1874802

Alternate ID(s):: OSTI ID: 1894258
OSTI ID: 2202743

Report Number(s):: IS-J--10,843

Journal Information:: Acta Materialia, Journal Name: Acta Materialia Vol. 236; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
Active learning
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Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

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