|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||An Interpretable Machine Learning Model to Predict Molten Salt Corrosion of Compositionally Complex Alloys and Facilitate Understanding of Novel Corrosion Mechanisms
||Bonita Goh, Yafei Wang, Phalgun Nelaturu, Michael Moorehead, Dan Thoma, Santanu Chaudhuri, Jason Hattrick-Simpers, Kumar Sridharan, Adrien Couet
|On-Site Speaker (Planned)
CCAs are of interest as structural materials in molten salt reactors because current alloys certified by ASME Sec(III) Div(5) code for their mechanical properties contain high Cr that are readily corrodible in molten halides due to the thermodynamic favorability of Cr corrosion in halides at nuclear reactor operating conditions. Corrosion of compositionally complex alloys (CCAs) in molten halides is not straightforward to predict because they do not possess one obvious base element whose kinetic and thermodynamic behavior provides the basis for prediction. The lack of prediction capability presents a bottleneck to search a quasi-infinite compositional space for a particular set of alloying elements for CCAs that could be suitable for molten salt reactor structural materials. We present a generalizable Random Forest Regressor (RFR) model trained and tested on 110 experimentally tested CCAs. Shapley analysis was used to interpret the model and extract alloy design parameters for optimizing corrosion resistance.