Molten Salt reactors require structural steels with improved corrosion-resistance. In order to identify steel compositions with improved corrosion-resistance without sacrificing hardness, melting point, resistance to embrittlement and other desirable mechanical properties, we seek to develop a methodology to automate the systematic production, analysis and classification of large sets of alloys.
The approach is to print alloys with different compositions as an array using additive manufacturing techniques. This array will be analyzed in-situ with an automated electrochemical probe. Information on physical descriptors will be obtained, coupled with feature identification and sample classification by machine learning techniques.
We seek to use this large set of data of correlated alloy composition and mechanical properties to understand the underlying basic science behind the material characteristics, and explain how the macro behavior is derived from microscopic structure.