Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields-Reference-Cited by-同舟云学术

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields

Published:2021-09-08 Issue:37 Volume:125 Page:10562-10570
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Lee Shao-Chun¹²^ORCID,Zhai Yanqin¹²^ORCID,Li Zhixia¹²,Walter Nathan P.¹²,Rose Melissa³,Heuser Brent J.¹,Z Y¹²⁴^ORCID

Affiliation:

1. Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States

2. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States

3. Division of Chemical and Fuel Cycle Technologies, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, United States

4. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States

Funder

Office of Nuclear Energy

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.1c05608

Reference39 articles.

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2. First-Principles Molecular Dynamics Simulations of UCln–NaCl (n = 3, 4) Molten Salts

3. First-principles molecular dynamics modeling of the LiCl–KCl molten salt system

4. Ionic sizes and born repulsive parameters in the NaCl-type alkali halides—II

5. XXIV. The sizes of ions and their influence on the properties of salt-like compounds

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