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Neural Network Potentials for Atomistic Simulations of Reactive Chemistry
Neural Network Potentials for Atomistic Simulations of Reactive Chemistry
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151923
- ISBN
- 9798383162989
- DDC
- 540
- 서명/저자
- Neural Network Potentials for Atomistic Simulations of Reactive Chemistry
- 발행사항
- [Sl] : University of Minnesota, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 101 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Goodpaster, Jason D.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2024.
- 초록/해제
- 요약Atomistic simulations play an important role in a wide range of chemical investigations, including studies of chemical kinetics. These simulations rely on accurate energies and forces, often obtained through expensive ab initio electronic structure calculations. Recently researchers have explored the use of machine learning models to provide analytical and differentiable potential energy surfaces for use in atomistic simulations. These ML models can provide energies at a fraction of the cost of ab initio methods and are also highly accurate within the chemical space represented in the training data. In this work, we explore methods for data sampling techniques for training datasets used to train ML potentials, specifically to calculate chemical kinetics of the OH+ CH4 hydrogen abstraction reaction. In addition, combined ML and molecular mechanics methods for condensed phase reactions is discussed.
- 일반주제명
- Chemistry
- 일반주제명
- Physical chemistry
- 일반주제명
- Computational chemistry
- 기타저자
- University of Minnesota Chemistry
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383162989
■035 ▼a(MiAaPQ)AAI31300122
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a540
■1001 ▼aGordon, Adrian M.
■24510▼aNeural Network Potentials for Atomistic Simulations of Reactive Chemistry
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a101 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Goodpaster, Jason D.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2024.
■520 ▼aAtomistic simulations play an important role in a wide range of chemical investigations, including studies of chemical kinetics. These simulations rely on accurate energies and forces, often obtained through expensive ab initio electronic structure calculations. Recently researchers have explored the use of machine learning models to provide analytical and differentiable potential energy surfaces for use in atomistic simulations. These ML models can provide energies at a fraction of the cost of ab initio methods and are also highly accurate within the chemical space represented in the training data. In this work, we explore methods for data sampling techniques for training datasets used to train ML potentials, specifically to calculate chemical kinetics of the OH+ CH4 hydrogen abstraction reaction. In addition, combined ML and molecular mechanics methods for condensed phase reactions is discussed.
■590 ▼aSchool code: 0130.
■650 4▼aChemistry
■650 4▼aPhysical chemistry
■650 4▼aComputational chemistry
■653 ▼aChemical kinetics
■653 ▼aChemical reactions
■653 ▼aMachine learning potentials
■653 ▼aAtomistic simulations
■690 ▼a0485
■690 ▼a0219
■690 ▼a0494
■71020▼aUniversity of Minnesota▼bChemistry.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162139▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
