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Theoretical study of the thermal conduction in Cu2S using machine learning-based interatomic potentials

Title
Theoretical study of the thermal conduction in Cu2S using machine learning-based interatomic potentials
Authors
Sanghui Kim
DGIST Authors
Kim, Sanghui; Lee, DooseokKang, Joongoo
Advisor(s)
강준구
Co-Advisor(s)
Doo Seok Lee
Issue Date
2019
Available Date
2019-10-03
Degree Date
2019-02
Type
Thesis
Table Of Contents
Abstract i List of contents ii List of figures iii 1. Introduction 1.1 Methods to calculate thermal conductivity 2 1.2 Solid-liquid hybrid phase Cu2S 3 2. Methods 2.1 Atomic simulation using machine learning 6 2.2 Green-Kubo method 12 3. Results and Discussion 3.1 Atomic energy 15 3.2 Thermal conductivity 18 4. Conclusion
URI
http://dgist.dcollection.net/common/orgView/200000171458
http://hdl.handle.net/20.500.11750/10704
DOI
10.22677/thesis.200000171458
Degree
MASTER
Department
Emerging Materials Science
University
DGIST
Related Researcher
  • Author Kang, Joongoo Computational Materials Theory Group
  • Research Interests Computational Materials Science & Materials Design; Nanomaterials for Energy Applications; Theoretical Condensed Matter Physics
Files:
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Collection:
Department of Emerging Materials ScienceThesesMaster


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