Predicting the High-Temperature Oxidation Response of Nickel Superalloys Using CALPHAD-Enhanced Machine Learning

Conference · Thu Mar 19 00:00:00 EDT 2026

DOI:https://doi.org/10.2172/3024055· OSTI ID:3024055

Sundar, Aditya ^[1]; Trehern, William ^[2]; Carney, Casey ^[1]; Oleksak, Richard ^[2]; Gao, Michael ^[2]

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1. NETL Site Support Contractor, National Energy Technology Laboratory
2. NETL

Structural materials such as Ni-based superalloys used in high-temperature power cycles are routinely exposed to toxic environments including high temperature and pressure, aqueous and gas corrosion, etc. Here, we present a physics-informed machine learning approach to predict the oxidation response of diverse Ni-superalloys. First, a high-fidelity experimental dataset is curated from typical oxidation mass-change experiments in air, covering 25+ elements and different physical behavior such as parabolic growth, non-parabolic growth, and oxide spallation. Second, the dataset is featurized using thermophysical, chemical, and mechanical properties obtained from high-throughput CALPHAD calculations. Third, several machine learning models are developed to identify key features related to mass-change characteristics and model the mass-change curve for various alloys. Finally, the model is deployed to rapidly screen over a new composition space and down-select candidate alloys with high oxidation resistance for experimental validation.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM); USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management (FE-20)
• OSTI ID: 3024055
• Resource Type: Conference presentation
• Conference Information: Conference Name: TMS 2026 Annual Meeting & Exhibition Location: San Diego, CA, United States Start Date: 3/15/2026 12:00:00 AM End Date: 3/19/2026 12:00:00 AM
• Country of Publication: United States
• Language: English

Related Subjects

CALPHAD
High-temperature oxidation
machine learning
Ni superalloys