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Updated: Jun 13, 2026

Determining Tribocorrosion Rate and Wear-Corrosion Synergy of Bulk and Thin Film Aluminum Alloys
Published on: September 11, 2018
Integrated High-Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-Resistant
Yafei Wang1, Bonita Goh1, Phalgun Nelaturu1
1Department of Engineering Physics, University of Wisconsin, Madison, WI, 53706, USA.
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 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 CrFeMnNi 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 CrFeMnNi 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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