Integrated High‐Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion‐Resistant Alloys-Reference-Cited by-同舟云学术

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

Published:2022-05-07 Issue:20 Volume:9 Page:2200370
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Advanced Science

Author:

Wang Yafei¹^ORCID,Goh Bonita¹,Nelaturu Phalgun¹,Duong Thien²,Hassan Najlaa¹,David Raphaelle¹,Moorehead Michael¹,Chaudhuri Santanu²,Creuziger Adam³,Hattrick‐Simpers Jason³,Thoma Dan J.¹⁴,Sridharan Kumar¹⁴,Couet Adrien¹⁴

Affiliation:

1. Department of Engineering Physics University of Wisconsin Madison WI 53706 USA

2. Applied Materials Division Argonne National Laboratory Lemont IL 60607 USA

3. National Institute of Standard and Technology Gaithersburg MD 20899 USA

4. Department of Materials Science and Engineering University of Wisconsin Madison WI 53706 USA

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202200370

Reference42 articles.

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2. Corrosion of alloys in a chloride molten salt (NaCl-LiCl) for solar thermal technologies

3. Calcium-based multi-element chemistry for grid-scale electrochemical energy storage

4. Corrosion and Thermal Stability of CrMnFeNi High Entropy Alloy in Molten FLiBe Salt

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