Machine Learning Vacancy Formation Energy in Nickel-Based Superalloys

Conference · Mon Apr 07 04:00:00 EDT 2025

DOI:https://doi.org/10.2172/2549191· OSTI ID:2549191

Sundar, Aditya ^[1]; San, Saro ^[1]; Gao, Michael ^[2]

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

Creep performance plays a key role in nickel-based superalloys for high temeprature applications. Creep behavior depends on many parameters such as strength, dislocations, diffusivity, and microstructural stability in addition to temeprature, applied stress, and oxidation. This work focuses on predicting vacancy formation energy in nickel-based superalloys using machine learning approach. High-throughput density functional theory (DFT) calculations are performed on Ni-based alloys with the addition of various alloying elements to predict the vacancy formation energy and vacancy concentration. Machine learning is performed using various models including graph neural networks.

• 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: 2549191
• Country of Publication: United States
• Language: English

Related Subjects

creep performance
nickel superalloy
vacancy