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Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence
Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151124
- ISBN
- 9798383183045
- DDC
- 574.191
- 저자명
- Wang, Dedi.
- 서명/저자
- Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence
- 발행사항
- [Sl] : University of Maryland, College Park, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 198 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Tiwary, Pratyush.
- 학위논문주기
- Thesis (Ph.D.)--University of Maryland, College Park, 2024.
- 초록/해제
- 요약Rapid advances in computational power have made all-atom molecular dynamics (MD) a powerful tool for studying systems in biophysics, chemical physics and beyond. By solving Newton's equations of motion in silico, MD simulations allow us to track the time evolution of complex molecular systems in an all-atom, femtosecond resolution, enabling the evaluation of both their thermodynamic and kinetic properties.Though MD simulations are powerful, their effectiveness is often hampered by the large amount of data they produce. For instance, a standard microsecond-long simulation of a protein can easily generate hundreds of gigabytes of data, which can be difficult to analyze. Moreover, the time required to conduct these simulations can be prohibitively long. Microsecond-long simulations often take weeks to complete, whereas the processes of interest may occur on the timescale of milliseconds or even hundreds of seconds. These factors collectively pose significant challenges in leveraging MD simulations for comprehensive analysis and exploration of chemical and biological systems.In this thesis, I address these challenges by leveraging physics-inspired insights to learn unique, useful, and also meaningful low-dimensional representations of complex molecular systems. These representations enable effective analysis and interpretation of the vast amount of data generated from experiments and simulations. These representations have proven to be valuable in providing mechanistic insights into some fundamental problems within theoretical chemistry and biophysics, such as understanding the interplay between long-range and short-range forces in ion pair dissociation and the transformation of proteins from unstable random coils to structured forms. Furthermore, these physics-informed representations play a crucial role in enhancing MD simulations. They facilitate the construction of simplified kinetic models, enabling the generation of dynamical trajectories spanning significantly longer time scales than those accessible by conventional MD simulations. Additionally, they can serve as blueprints to guide the sampling process in combination with existing enhanced sampling methods.Through this thesis, I showcase how the synergy between physics and AI can advance our understanding of molecular systems and facilitate more efficient and insightful analysis in the fields of computational chemistry and biophysics.
- 일반주제명
- Biophysics
- 일반주제명
- Statistical physics
- 일반주제명
- Computational chemistry
- 일반주제명
- Computational physics
- 키워드
- Machine learning
- 기타저자
- University of Maryland, College Park Biophysics (BIPH)
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383183045
■035 ▼a(MiAaPQ)AAI31146913
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574.191
■1001 ▼aWang, Dedi.▼0(orcid)0000-0002-7524-5809
■24510▼aAnalyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence
■260 ▼a[Sl]▼bUniversity of Maryland, College Park▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a198 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Tiwary, Pratyush.
■5021 ▼aThesis (Ph.D.)--University of Maryland, College Park, 2024.
■520 ▼aRapid advances in computational power have made all-atom molecular dynamics (MD) a powerful tool for studying systems in biophysics, chemical physics and beyond. By solving Newton's equations of motion in silico, MD simulations allow us to track the time evolution of complex molecular systems in an all-atom, femtosecond resolution, enabling the evaluation of both their thermodynamic and kinetic properties.Though MD simulations are powerful, their effectiveness is often hampered by the large amount of data they produce. For instance, a standard microsecond-long simulation of a protein can easily generate hundreds of gigabytes of data, which can be difficult to analyze. Moreover, the time required to conduct these simulations can be prohibitively long. Microsecond-long simulations often take weeks to complete, whereas the processes of interest may occur on the timescale of milliseconds or even hundreds of seconds. These factors collectively pose significant challenges in leveraging MD simulations for comprehensive analysis and exploration of chemical and biological systems.In this thesis, I address these challenges by leveraging physics-inspired insights to learn unique, useful, and also meaningful low-dimensional representations of complex molecular systems. These representations enable effective analysis and interpretation of the vast amount of data generated from experiments and simulations. These representations have proven to be valuable in providing mechanistic insights into some fundamental problems within theoretical chemistry and biophysics, such as understanding the interplay between long-range and short-range forces in ion pair dissociation and the transformation of proteins from unstable random coils to structured forms. Furthermore, these physics-informed representations play a crucial role in enhancing MD simulations. They facilitate the construction of simplified kinetic models, enabling the generation of dynamical trajectories spanning significantly longer time scales than those accessible by conventional MD simulations. Additionally, they can serve as blueprints to guide the sampling process in combination with existing enhanced sampling methods.Through this thesis, I showcase how the synergy between physics and AI can advance our understanding of molecular systems and facilitate more efficient and insightful analysis in the fields of computational chemistry and biophysics.
■590 ▼aSchool code: 0117.
■650 4▼aBiophysics
■650 4▼aStatistical physics
■650 4▼aComputational chemistry
■650 4▼aComputational physics
■653 ▼aMachine learning
■653 ▼aMolecular dynamics
■653 ▼aRepresentation learning
■653 ▼aStatistical mechanics
■653 ▼aTheoretical chemistry
■690 ▼a0786
■690 ▼a0800
■690 ▼a0217
■690 ▼a0216
■690 ▼a0219
■71020▼aUniversity of Maryland, College Park▼bBiophysics (BIPH).
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0117
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160840▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
