Integrated High-throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-resistant Alloys

Published

May 7, 2022

Author(s)

Yafei Wang, Bonita Goh, Phalgun Nelaturu, Thien Duong, Najlaa Hassan, Raphaelle David, Michael Moorehead, Santanu Chaudhuri, Adam Abel Creuziger, Jason Hattrick-Simpers, Dan Thoma, Kumar Sridharan, Adrien Couet

Abstract

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here we present an integrated approach using a set of high-throughput alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys were evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by thousands of times. Based on the obtained results, we unveiled a sacrificial mechanism in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.

Citation

Advanced Science

Pub Type

Journals

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Citation

Wang, Y. , Goh, B. , Nelaturu, P. , Duong, T. , Hassan, N. , David, R. , Moorehead, M. , Chaudhuri, S. , Creuziger, A. , Hattrick-Simpers, J. , Thoma, D. , Sridharan, K. and Couet, A. (2022), Integrated High-throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-resistant Alloys, Advanced Science, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932593 (Accessed December 13, 2024)