|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Machine learning guided descriptor selection for predicting corrosion resistance in multi-principal element alloys (MPEAs)
||Ankit Roy, M. F. N. Taufique, Hrishabh Khakurel, Ram Devanathan, Duane D. Johnson, Ganesh Balasubramanian
|On-Site Speaker (Planned)
More than $ 270 billion is spent on combatting corrosion annually in the USA alone. This work uses machine learning for the development of highly corrosion resistant alloys. The focus is on a new class of alloys called Multi-Principal-Element Alloys (MPEAs) as a potential solution. MPEAs are composed of multiple elements (4 or more) with arbitrary proportions. Some MPEAs exhibit excellent mechanical properties at high temperatures but their design-search space is half a trillion combinations. To overcome this challenge, we employ machine-learning tools to develop a model that predicts the corrosion resistance of any given MPEA, supported by existing (but limited) corrosion data. Such a model reveals important features that determine the corrosion resistance of a given alloy and serves as a tool for swiftly screening a vast number of MPEAs and selecting the best corrosion-resistant systems.
||High-Entropy Alloys, Machine Learning,