About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond III
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Presentation Title |
An Automated, Machine Learning-driven Framework for Predicting High Temperature Oxidation Properties in Refractory Complex, Concentrated Alloys |
Author(s) |
Sharmila Karumuri, Saswat Mishra, Vincent Mika, Collin Scott, Nimish Awalgaonkar, Austin Hernandez, Kenneth Sandhage, Ilias Bilionis, Alejandro Strachan, Michael S. Titus |
On-Site Speaker (Planned) |
Michael S. Titus |
Abstract Scope |
Refractory complex, concentrated alloys (RCCAs) offer new avenues for designing high strength and oxidation resistant materials at elevated temperature. However, RCCAs often exhibit multiple oxidation mechanisms including oxide volatilization, internal oxidation, external scale formation, and pesting. These overlapping and complex mechanisms has impeded efforts to predict oxidation behavior based on alloy compositions. In this presentation, we will present our recent efforts to create an open-source, automated framework housed in NanoHub for experimental data ingestion and storage of high temperature oxidation experimental data. During data ingestion, this framework automatically fits oxidation mass gain curves and extracts oxidation model parameters with quantified uncertainty. So far, more than 100 unique compositions and 380 unique experimental mass gain curves have been collected and analyzed. Predictions of mass gain behavior of RCCAs utilizing machine learning with physics-based descriptors will be presented, and recent efforts to design new RCCAs with superior oxidation resistance will be shown. |