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Ab Initio Multi-Scale Modeling of Crystals: Methods and Applications in Ferroelectrics
Ab Initio Multi-Scale Modeling of Crystals: Methods and Applications in Ferroelectrics
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152028
- ISBN
- 9798384463481
- DDC
- 519
- 저자명
- Xie, Pinchen.
- 서명/저자
- Ab Initio Multi-Scale Modeling of Crystals: Methods and Applications in Ferroelectrics
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Car, Roberto;E., Weinan.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약The ab initio density functional theory (DFT), all-atom molecular dynamics (MD), and coarse-grained dynamics are effective physical models bridging the microscale with the mesoscale. The Born-Oppenheimer approximation and the Mori-Zwanzig formalism indicate the conceptual consistency among these models. However, in multiscale physical modeling, the numerical consistency among these models is still a long-term pursuit. Machine learning addresses this issue by parameterizing a coarse-grain model with data provided by a fine-grain model.We apply the data-driven approach to the multiscale modeling of crystalline material and use ferroelectrics for demonstration. We use machine-learned potential energy surface and polarization surface to bridge DFT and all-atom MD. Then, we propose a machine-learned generalized Langevin equation to bridge all-atom MD and coarse-grained lattice dynamics. Consistency on static and dynamical material properties is demonstrated for the prototypical ferroelectric material lead titanate by modeling its paraelectric-ferroelectric phase transition and domain motion. The methodologies described can be readily applied to a lot of other crystals.
- 일반주제명
- Applied mathematics
- 일반주제명
- Physics
- 일반주제명
- Chemistry
- 일반주제명
- Materials science
- 키워드
- Crystals
- 키워드
- Machine learning
- 기타저자
- Princeton University Applied and Computational Mathematics
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152028
■006m o d
■007cr#unu||||||||
■020 ▼a9798384463481
■035 ▼a(MiAaPQ)AAI31333591
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aXie, Pinchen.▼0(orcid)0000-0002-9330-4032
■24510▼aAb Initio Multi-Scale Modeling of Crystals: Methods and Applications in Ferroelectrics
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Car, Roberto;E., Weinan.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aThe ab initio density functional theory (DFT), all-atom molecular dynamics (MD), and coarse-grained dynamics are effective physical models bridging the microscale with the mesoscale. The Born-Oppenheimer approximation and the Mori-Zwanzig formalism indicate the conceptual consistency among these models. However, in multiscale physical modeling, the numerical consistency among these models is still a long-term pursuit. Machine learning addresses this issue by parameterizing a coarse-grain model with data provided by a fine-grain model.We apply the data-driven approach to the multiscale modeling of crystalline material and use ferroelectrics for demonstration. We use machine-learned potential energy surface and polarization surface to bridge DFT and all-atom MD. Then, we propose a machine-learned generalized Langevin equation to bridge all-atom MD and coarse-grained lattice dynamics. Consistency on static and dynamical material properties is demonstrated for the prototypical ferroelectric material lead titanate by modeling its paraelectric-ferroelectric phase transition and domain motion. The methodologies described can be readily applied to a lot of other crystals.
■590 ▼aSchool code: 0181.
■650 4▼aApplied mathematics
■650 4▼aPhysics
■650 4▼aChemistry
■650 4▼aMaterials science
■653 ▼aDensity functional theory
■653 ▼aMolecular dynamics
■653 ▼aCrystals
■653 ▼aMachine learning
■653 ▼aCrystalline material
■690 ▼a0364
■690 ▼a0605
■690 ▼a0485
■690 ▼a0794
■71020▼aPrinceton University▼bApplied and Computational Mathematics.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162571▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
