ASME Sec(III) Div(5) code-certified structural alloys for Molten (halide) Salt Reactors (MSRs) such as 800H, SS316, and IN617 have significant Cr content. This makes them readily-degradable in molten halide salts due to the thermodynamic favorability of soluble chromium halide formation. It is therefore imperative to discover alloys with corrosion resistance that exceeds those of currently-certified alloys, which also possess the necessary high hardness and irradiation-resistance at reactor operating temperatures as a prerequisite for the licensing and deployment of MSRs.

The largely-unexplored quasi-infinite quarternary FeCrMnNi High Entropy Alloy space shows promise to yield desired alloys. We present a high-throughput (HTP) process demonstrating a turnaround time from fabrication to corrosion-testing and analysis of 1 week for 25 samples. 70 alloy samples of 1cm² were each corrosion-tested on 0.3g salt droplets in isolated corrosion environments. The HTP platform includes the development of an in-situ high-temperature electrochemical sensor system capable of automating the analysis of dissolved corrosion product analytes in the salt.

The results of the 70 corrosion tests were used to train a Machine Learning model to predict corrosion performance metrics (eg. Elemental corrosion concentration into salt) based on an input vector parametrizing the physical properties (“descriptors”) of the alloys in the sample set. The model was tested on 20 additional samples and demonstrated conservative predictive capability as well as facilitated the reduction of input feature space from 62 dimensions to 4 dimensions with negligible loss in predictive accuracy.