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Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design
Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152830
- ISBN
- 9798346532736
- DDC
- 542
- 서명/저자
- Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design
- 발행사항
- [Sl] : Harvard University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 129 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
- 주기사항
- Advisor: Kozinsky, Boris.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2024.
- 초록/해제
- 요약Computational materials science enables unique insight into atomistic processes that cannot be probed with current experimental apparatuses. For decades, the field has offered new understanding in a variety of material domains, ranging from polymers, proteins, and biophysics to battery materials, solvents, and many others. Of particular interest are molecular dynamics studies that can be used to analyze the kinetic and thermodynamic behavior of such systems.Machine learning has shown to be a valuable tool in modeling the potential energy surface required for these methods. Despite significant progress in both the hardware, software, and theoretical capabilities of molecular dynamics approaches and machine learning, studying atomistic processes with long spatial and temporal scales at full atomistic resolution becomes prohibitive. To this end, coarse graining is a crucial alternative that enables the study of these systems with more fine tuned control than can be achieved in experimental setups, while also retaining a higher degree of spatial and temporal fidelity than would be possible experimentally. In this dissertation, I will introduce a flexible Bayesian force field approach to design coarse grained free energy models. These novel methods enable an automated approach to the data collection process, while most importantly allowing for highly transferable models. I will further demonstrate how new approaches that utilize the integration of physics principles can provide more accurate and robust machine learning models for coarse graining applications. Finally, I will discuss on-going and future developments, applications, and considerations for the future of computational materials exploration using these scalable and accurate methodological advancements.
- 일반주제명
- Computational chemistry
- 일반주제명
- Computational physics
- 일반주제명
- Materials science
- 키워드
- Coarse graining
- 키워드
- Force fields
- 키워드
- Machine learning
- 기타저자
- Harvard University Engineering and Applied Sciences - Applied Physics
- 기본자료저록
- Dissertations Abstracts International. 86-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152830
■006m o d
■007cr#unu||||||||
■020 ▼a9798346532736
■035 ▼a(MiAaPQ)AAI31560473
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a542
■1001 ▼aDuschatko, Blake R.▼0(orcid)0009-0008-8632-3199
■24510▼aMachine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design
■260 ▼a[Sl]▼bHarvard University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a129 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-05, Section: B.
■500 ▼aAdvisor: Kozinsky, Boris.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2024.
■520 ▼aComputational materials science enables unique insight into atomistic processes that cannot be probed with current experimental apparatuses. For decades, the field has offered new understanding in a variety of material domains, ranging from polymers, proteins, and biophysics to battery materials, solvents, and many others. Of particular interest are molecular dynamics studies that can be used to analyze the kinetic and thermodynamic behavior of such systems.Machine learning has shown to be a valuable tool in modeling the potential energy surface required for these methods. Despite significant progress in both the hardware, software, and theoretical capabilities of molecular dynamics approaches and machine learning, studying atomistic processes with long spatial and temporal scales at full atomistic resolution becomes prohibitive. To this end, coarse graining is a crucial alternative that enables the study of these systems with more fine tuned control than can be achieved in experimental setups, while also retaining a higher degree of spatial and temporal fidelity than would be possible experimentally. In this dissertation, I will introduce a flexible Bayesian force field approach to design coarse grained free energy models. These novel methods enable an automated approach to the data collection process, while most importantly allowing for highly transferable models. I will further demonstrate how new approaches that utilize the integration of physics principles can provide more accurate and robust machine learning models for coarse graining applications. Finally, I will discuss on-going and future developments, applications, and considerations for the future of computational materials exploration using these scalable and accurate methodological advancements.
■590 ▼aSchool code: 0084.
■650 4▼aComputational chemistry
■650 4▼aComputational physics
■650 4▼aMaterials science
■653 ▼aCoarse graining
■653 ▼aForce fields
■653 ▼aInteratomic potentials
■653 ▼aMachine learning
■690 ▼a0219
■690 ▼a0216
■690 ▼a0794
■71020▼aHarvard University▼bEngineering and Applied Sciences - Applied Physics.
■7730 ▼tDissertations Abstracts International▼g86-05B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164085▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
