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

Area of Science:

• Materials Science and Engineering
• Corrosion Science
• Computational Materials Science

Background:

• Molten salt technologies offer significant societal benefits but are hindered by a lack of suitable corrosion-resistant alloys.
• Understanding and mitigating corrosion in high-temperature molten salt environments is critical for technological advancement.

Purpose of the Study:

• To accelerate the development of corrosion-resistant alloys for molten salt applications.
• To gain fundamental insights into the mechanisms of molten salt corrosion.
• To establish a framework for designing advanced high-temperature alloys.

Main Methods:

• Integrated approach combining high-throughput (HTP) alloy synthesis and corrosion testing.
• Utilized automated characterization and machine learning for data analysis.
• Evaluated a wide range of Cr-Fe-Mn-Ni alloys for molten salt corrosion resistance.

Main Results:

• Achieved acceleration of corrosion-resistant alloy development by 2 to 3 orders of magnitude.
• Identified a sacrificial protection mechanism in Cr-Fe-Mn-Ni alloys during molten salt corrosion.
• Demonstrated the effectiveness of this mechanism in preventing depletion of less stable alloy elements.

Conclusions:

• The integrated HTP approach significantly speeds up alloy discovery for molten salt environments.
• The unveiled sacrificial protection mechanism provides crucial insights for designing superior corrosion-resistant alloys.
• This study paves the way for the broader implementation of advanced molten salt technologies.