Magnetic Hamiltonian parameter estimation using deep learning techniques-Reference-Cited by-同舟云学术

Magnetic Hamiltonian parameter estimation using deep learning techniques

Author:

Kwon H. Y.¹^ORCID,Yoon H. G.²,Lee C.²^ORCID,Chen G.³,Liu K.³⁴^ORCID,Schmid A. K.⁵^ORCID,Wu Y. Z.⁶^ORCID,Choi J. W.¹,Won C.²^ORCID

Affiliation:

1. Center for Spintronics, Korea Institute of Science and Technology, Seoul 02792, South Korea.

2. Department of Physics, Kyung Hee University, Seoul 02447, South Korea.

3. Department of Physics, University of California, Davis, Davis, CA 95616, USA.

4. Physics Department, Georgetown University, Washington, DC 20057, USA.

5. National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

6. Department of Physics, State Key Laboratory of Surface Physics and Advanced Materials Laboratory, Fudan University, Shanghai 200433, China.

Abstract

Deep learning can provide a better complementary link between the theoretical models and experimental results in magnetism.

Funder

National Science Foundation

Semiconductor Research Corporation

National Research Foundation of Korea

National Research Council of Science and Technology

US Department of Energy

National Science Foundation of China

UC Office of the President Multicampus Research Programs and Initiatives

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference39 articles.

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