Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction-Reference-Cited by-同舟云学术

Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction

Published:2022-07-22 Issue:1 Volume:106 Page:
ISSN:2469-9950
Container-title:Physical Review B
language:en
Short-container-title:Phys. Rev. B

Author:

Guo Jicheng^ORCID,Ward Logan,Babuji Yadu,Hoyt Nathaniel,Williamson Mark,Foster Ian^ORCID,Jackson Nicholas^ORCID,Benmore Chris^ORCID,Sivaraman Ganesh^ORCID

Funder

Laboratory Directed Research and Development

U.S. Department of Energy

Office of Science

Argonne National Laboratory

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.106.014209/fulltext

Reference76 articles.

1. From Molten Salts to Ionic Liquids: A “Nano” Journey

2. Liquid Metal Batteries: Past, Present, and Future

3. The US Department of Energy’s high temperature reactor research and development program – Progress as of 2019

4. Multielectrode array sensors to enable long-duration corrosion monitoring and control of concentrating solar power systems

5. Molten salt eutectics from atomistic simulations

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