Integrated High‐Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion‐Resistant Alloys. Issue 20 (7th May 2022) - Wang, Yafei; Goh, Bonita; Nelaturu, Phalgun; Duong, Thien; Hassan, Najlaa; David, Raphaelle; Moorehead, Michael; Chaudhuri, Santanu; Creuziger, Adam; Hattrick‐Simpers, Jason; Thoma, Dan J.; Sridharan, Kumar; et al.
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 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. Abstract : An integrated approach using a set of high‐throughput alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning is developed to accelerate the molten salt corrosion‐resistant alloy development for molten salt applications by 2 to 3 orders of magnitude.