CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

Sundar, Aditya; Tan, Xingru; Hu, Shanshan; Gao, Michael C.

doi:10.1557/s43578-024-01489-0

CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

Journal Article · Fri Jan 31 00:00:00 EST 2025 · Journal of Materials Research

DOI:https://doi.org/10.1557/s43578-024-01489-0· OSTI ID:2514547

^[1]; Tan, Xingru ^[2]; Hu, Shanshan ^[2]; ^[1]

National Energy Technology Lab. (NETL), Albany, OR (United States)
West Virginia Univ., Morgantown, WV (United States)

Two crucial properties influencing the performance of high-temperature alloys are coefficient of thermal expansion (CTE) and phase constitution. It is desirable to have alloys with low CTE, which reduces CTE mismatch with the surface oxide and the likelihood of oxide spallation. Reducing the amount of brittle intermetallic phases such as Sigma (σ) enhances alloy ductility and processability. Here, we propose a multi-objective Bayesian Optimization (BO) model to simultaneously minimize CTE (at an operational temperature of 1150 °C) and T_σ (temperature when the Sigma phase completely dissolves in the metal matrix), properties which are obtained from high-throughput CALculation of PHAse Diagrams (CALPHAD). The model successfully identifies several alloys with CTE ≤ 2 × 10^–5/K and T_σ ≤ 500 °C by exploring just 7% of the nickel–chromium–cobalt–aluminum–iron (Ni–Cr–Co–Al–Fe) composition space. Such multi-objective alloy design frameworks can be used to inform additive manufacturing experiments and accelerate alloy discovery for high-temperature energy applications.

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Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy and Carbon Management (FECM)

Grant/Contract Number:: EE0010214

OSTI ID:: 2514547

Alternate ID(s):: OSTI ID: 2556865

Journal Information:: Journal of Materials Research, Journal Name: Journal of Materials Research Journal Issue: 1 Vol. 40; ISSN 0884-2914

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

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