4.8 Article

Integrated High-Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-Resistant Alloys

Journal

ADVANCED SCIENCE
Volume 9, Issue 20, Pages -

Publisher

WILEY
DOI: 10.1002/advs.202200370

Keywords

additive manufacturing; corrosion; high-throughput methods; machine learning; molten salt

Funding

  1. Advanced Research Projects Agency-Energy (ARPA-E) [DE-AR0001050]

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This article presents an integrated approach using high-throughput alloy synthesis, corrosion testing, and modeling to accelerate the development of corrosion-resistant alloys for molten salt applications. It also uncovers a sacrificial protection mechanism in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts, providing new insights for the design of high-temperature molten salt corrosion-resistant alloys.
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 an integrated approach is presented using a set of high-throughput (HTP) 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 are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled 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.

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