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Developing Density Functional Theory With Physical Prior Knowledge- [electronic resource]
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Developing Density Functional Theory With Physical Prior Knowledge- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101637
- ISBN
- 9798380130714
- DDC
- 530
- 저자명
- Pederson, Ryan.
- 서명/저자
- Developing Density Functional Theory With Physical Prior Knowledge - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Irvine., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(224 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
- 주기사항
- Advisor: Burke, Kieron.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Irvine, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Density functional theory (DFT) has been used extensively over the past several decades and across many branches of science. The success of DFT lies in its relatively low-cost and usefully high accuracy in many practical systems of interest. However, there are still many instances, such as strongly correlated systems or systems at high temperatures, where conventional DFT approaches are no longer reliable. In addition, reliable DFT approaches are often computationally intractable for large system sizes, limiting their scope of application in realistic system settings. This dissertation is a collection of my contributions to address these fundamental challenges in the field. A common theme across all projects is the use of physical prior knowledge to motivate or (in)directly constrain the methods and techniques developed. In Chapter 1, I provide context for the research presented in the following self-contained chapters. Chapter 2 introduces condition probability DFT (CP-DFT) as a new and alternative density functional approach to obtain conditional probability densities and ground-state energies. Chapter 3 expands upon the previous chapter by establishing CP-DFT as a formally exact theory and derives several key physical properties of CP densities and corresponding potentials used in the theory. Chapter 4 analyzes and discusses the role of exact physical conditions (constraints) in developing conventional Kohn-Sham DFT exchange-correlation (XC) approximations. Chapter 5 introduces the Kohn-Sham regularizer method for training neural network-based XC models for strongly correlated systems. Chapter 6 expands on the previous chapter by developing a spin-adapted Kohn-Sham regularizer and demonstrating impressive generalizability on weakly correlated systems. Finally, Chapter 7 explores the repurposing of Tensor Processing Units - hardware designed for machine-learning tasks - for large-scale DFT calculations by utilizing algorithms that exploit physical properties of the density matrix.
- 일반주제명
- Theoretical physics.
- 키워드
- Density matrix
- 기타저자
- University of California, Irvine Physics
- 기본자료저록
- Dissertations Abstracts International. 85-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101637
■006m o d
■007cr#unu||||||||
■020 ▼a9798380130714
■035 ▼a(MiAaPQ)AAI30632017
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aPederson, Ryan.
■24510▼aDeveloping Density Functional Theory With Physical Prior Knowledge▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Irvine. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(224 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-02, Section: B.
■500 ▼aAdvisor: Burke, Kieron.
■5021 ▼aThesis (Ph.D.)--University of California, Irvine, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aDensity functional theory (DFT) has been used extensively over the past several decades and across many branches of science. The success of DFT lies in its relatively low-cost and usefully high accuracy in many practical systems of interest. However, there are still many instances, such as strongly correlated systems or systems at high temperatures, where conventional DFT approaches are no longer reliable. In addition, reliable DFT approaches are often computationally intractable for large system sizes, limiting their scope of application in realistic system settings. This dissertation is a collection of my contributions to address these fundamental challenges in the field. A common theme across all projects is the use of physical prior knowledge to motivate or (in)directly constrain the methods and techniques developed. In Chapter 1, I provide context for the research presented in the following self-contained chapters. Chapter 2 introduces condition probability DFT (CP-DFT) as a new and alternative density functional approach to obtain conditional probability densities and ground-state energies. Chapter 3 expands upon the previous chapter by establishing CP-DFT as a formally exact theory and derives several key physical properties of CP densities and corresponding potentials used in the theory. Chapter 4 analyzes and discusses the role of exact physical conditions (constraints) in developing conventional Kohn-Sham DFT exchange-correlation (XC) approximations. Chapter 5 introduces the Kohn-Sham regularizer method for training neural network-based XC models for strongly correlated systems. Chapter 6 expands on the previous chapter by developing a spin-adapted Kohn-Sham regularizer and demonstrating impressive generalizability on weakly correlated systems. Finally, Chapter 7 explores the repurposing of Tensor Processing Units - hardware designed for machine-learning tasks - for large-scale DFT calculations by utilizing algorithms that exploit physical properties of the density matrix.
■590 ▼aSchool code: 0030.
■650 4▼aCondensed matter physics.
■650 4▼aTheoretical physics.
■653 ▼aDensity functional theory
■653 ▼aDensity matrix
■653 ▼aPhysical conditions
■653 ▼aFundamental challenges
■653 ▼aCorrelated systems
■690 ▼a0611
■690 ▼a0753
■71020▼aUniversity of California, Irvine▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g85-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0030
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934643▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
