| Abstract Scope |
There are many degrees of freedom associated with multi-principal element alloys (MPEAs). Thisis enabled by the wide variety of alloying elements and atomic structures available to produce alarge number of single-phase, solid solution, or multi-phase alloys. Consequently, there is thepossibility of producing a wide range of material properties, often unique. However, a structure,composition, and processing paradigm governing the corrosion properties of MPEAs has not yetemerged. Corrosion properties depend on many factors and resultant properties are difficult topredict in such complex systems. The quest for superior corrosion properties requires anunderstanding of the fate of each element during corrosion, the functions of elements whetherin the alloy, substrate or both, and the ability to harness interactive effects. One example of thelatter is the beneficial “3rd element effect” which can improve passivity and oxidation behavioreven at low passivator alloying contents. Large gaps in knowledge still exist regarding the (a) specific functions of each element, (b) effects of elements in unusual combinations, and (c) possible formation of complex protective oxides that regulate corrosion and breakdown. Several strategies to address these concerns are underway. These include but are not limited to high throughput experimental testing, use of CALPHAD and other thermodynamic tools as well as machine learning. Guiding principles relevant to specific alloy families are beginning to emerge such as the identification of certain specific elemental roles, functions, and types of nearest neighbor atomic interactions which may be beneficial. Machine learning methods have yielded robust and generalizable predictive methods, enhancing downselection. These issues are discussed with the goal of accelerating the understanding of the corrosion behavior in this class of materials. |