Multi-objective Bayesian alloy design using multi-task Gaussian processes

Journal Article · 19 August 2023 · Materials Letters

DOI: https://doi.org/10.1016/j.matlet.2023.135067 · OSTI ID:2202735

Khatamsaz, Danial  ^[1]; Vela, Brent  ^[2]; Arróyave, Raymundo  ^[2]

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1. Texas A&M University, College Station, TX (United States); Texas A&M University
2. Texas A&M University, College Station, TX (United States)

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

• Research Organization: Texas A&M University, College Station, TX (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E); National Science Foundation (NSF)
• Grant/Contract Number: AR0001427
• OSTI ID: 2202735
• Journal Information: Materials Letters, Journal Name: Materials Letters Vol. 351; ISSN 0167-577X
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English