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Nanotubes from First Principle and Data-Driven Methods: Real and Reimagined
Nanotubes from First Principle and Data-Driven Methods: Real and Reimagined
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151045
- ISBN
- 9798381967210
- DDC
- 620.11
- 저자명
- Yu, Hsuan Ming.
- 서명/저자
- Nanotubes from First Principle and Data-Driven Methods: Real and Reimagined
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 211 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
- 주기사항
- Advisor: Banerjee, Amartya.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약First principle methods and data-driven approach were employed to investigate the intricate mechanical and electrical behaviors and properties of various nanotubes, both real and reimagined. A real space, helical and cyclic symmetry adapted, Kohn Sham DFT code- HelicalDFT was utilized to explore the torsional, extensional and electronic properties of group-IV nanotubes, unveiling unique mechanical and electronic responses. The study further delves into Carbon Kagome Nanotubes (CKNTs) and novel P2C3 nanotubes, highlighting their potential in material science due to distinctive electronic characteristics. A data- driven approach using machine learning predicts the electronic structure of nanotubes even under deformation, enhancing the understanding of nanomaterial behavior. Additionally, the integration of ellipsoidal coordinate systems within the current computational frame- work to advance in future evaluation of Gaussian curvature effects, opening new avenues for the design of nanomaterials with customized properties. This comprehensive research provides valuable insights into nanotube properties, offering a robust framework for future material science explorations and technological applications.
- 일반주제명
- Materials science
- 일반주제명
- Nanoscience
- 일반주제명
- Applied physics
- 키워드
- Nanotubes
- 키워드
- Machine learning
- 키워드
- Cyclic symmetry
- 기타저자
- University of California, Los Angeles Materials Science and Engineering 0328
- 기본자료저록
- Dissertations Abstracts International. 85-09B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798381967210
■035 ▼a(MiAaPQ)AAI31140760
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620.11
■1001 ▼aYu, Hsuan Ming.
■24510▼aNanotubes from First Principle and Data-Driven Methods: Real and Reimagined
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a211 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-09, Section: B.
■500 ▼aAdvisor: Banerjee, Amartya.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aFirst principle methods and data-driven approach were employed to investigate the intricate mechanical and electrical behaviors and properties of various nanotubes, both real and reimagined. A real space, helical and cyclic symmetry adapted, Kohn Sham DFT code- HelicalDFT was utilized to explore the torsional, extensional and electronic properties of group-IV nanotubes, unveiling unique mechanical and electronic responses. The study further delves into Carbon Kagome Nanotubes (CKNTs) and novel P2C3 nanotubes, highlighting their potential in material science due to distinctive electronic characteristics. A data- driven approach using machine learning predicts the electronic structure of nanotubes even under deformation, enhancing the understanding of nanomaterial behavior. Additionally, the integration of ellipsoidal coordinate systems within the current computational frame- work to advance in future evaluation of Gaussian curvature effects, opening new avenues for the design of nanomaterials with customized properties. This comprehensive research provides valuable insights into nanotube properties, offering a robust framework for future material science explorations and technological applications.
■590 ▼aSchool code: 0031.
■650 4▼aMaterials science
■650 4▼aNanoscience
■650 4▼aApplied physics
■653 ▼aDensity functional theory
■653 ▼aFirst principle method
■653 ▼aNanotubes
■653 ▼aMachine learning
■653 ▼aCyclic symmetry
■690 ▼a0794
■690 ▼a0565
■690 ▼a0215
■71020▼aUniversity of California, Los Angeles▼bMaterials Science and Engineering 0328.
■7730 ▼tDissertations Abstracts International▼g85-09B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160592▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
