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

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. ; This is a manuscript of an article published as Khatamsaz, Danial, Brent Vela, Prashant Singh, Duane D. Johnson, Douglas Allaire, and Raymundo Arróyave. "Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys." Acta Materialia 236 (2022): 118133. DOI:10.1016/j.actamat.2022.118133. Copyright 2022 Acta Materialia Inc. Posted with permission. DOE Contract Number(s): AR0001427; AC02-07CH11358; DGE-1545403; CDSE-2001333; CISE-1835690; DMREF-2119103.