Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys

Roy, Ankit
Taufique, M. F. N.
Khakurel, Hrishabh
Devanathan, Ram
Johnson, Duane
Balasubramanian, Ganesh
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Iowa State University Digital Repository, Ames IA (United States)
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Materials Science and EngineeringChemical and Biological EngineeringAmes LaboratoryPhysics and Astronomy
More than $270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (Δa) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys.
This article is published as Roy, Ankit, M. F. N. Taufique, Hrishabh Khakurel, Ram Devanathan, Duane D. Johnson, and Ganesh Balasubramanian. "Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys." npj Materials Degradation 6, no. 1 (2022): 1-10. DOI: 10.1038/s41529-021-00208-y. Copyright 2022 Battelle Memorial Institute. Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC05-76RL01830; AC02-07CH11358.