DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning-Reference-Cited by-同舟云学术

DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning

Published:2020-11-12 Issue:47 Volume:124 Page:25760-25768
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Tovey Samuel¹^ORCID,Narayanan Krishnamoorthy Anand²^ORCID,Sivaraman Ganesh³⁴,Guo Jicheng⁵^ORCID,Benmore Chris⁶^ORCID,Heuer Andreas²⁷,Holm Christian¹^ORCID

Affiliation:

1. Institute for Computational Physics, University of Stuttgart, Allmandring 3, 70569 Stuttgart, Germany

2. Helmholtz-Institute Münster: Ionics in Energy Storage (IEK-12), Forschungszentrum Jülich GmbH, Corrensstraße 46, 48149 Münster, Germany

3. Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, United States

4. Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

5. Chemical and Fuel Cycle Technologies Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

6. X-ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

7. Institute of Physical Chemistry, University of Münster, Corrensstraße 28/30, 48149 Münster, Germany

Funder

Deutsche Forschungsgemeinschaft

Max-Planck-Gesellschaft

Office of Science

Argonne National Laboratory

Max Planck Institute for Condensed Matter Physics

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.0c08870

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