About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
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Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Random Forest Regressor Models for the Prediction of Novel Alloy Corrosion Performance |
Author(s) |
Bonita Goh, Yafei Wang, Phalgun Nelaturu, Thien Duong, Dan Thoma, Jason Hattrick-Simpers, Santanu Chaudhuri, Adrien Couet |
On-Site Speaker (Planned) |
Bonita Goh |
Abstract Scope |
The FeCrMnNi High Entropy Alloy space shows promise to yield compositions that possess the set of desired properties for next-generation molten salt-based energy systems. However, the combinatorial quarternary composition space is quasi-infinite, which poses challenges to alloy optimization. Using a unique and relatively large set of corrosion data obtained from a high-throughput high-temperature corrosion testing platform developed to train machine learning models, we present Random Forest Regressor models for predicting corrosion performance metrics (eg. dissolved corrosion product concentration in the salt) based on an input vector parametrizing the alloys’ physical properties . The strength of this approach is that importance score rankings of alloy physical descriptors can help to affirm observations of novel corrosion mechanisms. In addition, the model facilitates backwards-projection to determine compositions that have yet to be tested but show promise, helping us efficiently search the composition space for promising alloy corrosion resistance. |
Proceedings Inclusion? |
Planned: |
Keywords |
Powder Materials, High-Entropy Alloys, High-Temperature Materials |