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Machine Learning Meets Statistical Mechanics: Exploring Phase Behavior Through Local Affinity
Machine Learning Meets Statistical Mechanics: Exploring Phase Behavior Through Local Affinity
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104703
- ISBN
- 9798280771079
- DDC
- 540
- 저자명
- Jang, Inhyuk.
- 서명/저자
- Machine Learning Meets Statistical Mechanics: Exploring Phase Behavior Through Local Affinity
- 발행사항
- [Sl] : The University of Wisconsin - Madison, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 135 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Yethiraj, Arun.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
- 초록/해제
- 요약Sudden changes in the states of matter, including phase and structural transitions, are fundamental phenomena observed across physical, chemical, and biological systems, yet their underlying mechanisms remain only partially understood-particularly in complex, multiscale, or farfrom-equilibrium environments. This work focuses on addressing these challenges through a combination of computational modeling and unsupervised machine learning (UML). In particular, I investigate dendrite formation in lithium metal batteries using stochastic dynamics simulations to explore morphological transitions and identify key factors influencing their growth. Beyond this, I aim to develop new methodologies for analyzing liquid-liquid phase separation (LLPS) in complex mixtures such as bio condensates and polyelectrolyte solutions, where conventional theories often fall short. By implementing UML, I construct data-driven phase diagrams and introduce a novel order parameter, local affinity, which encodes phase information as binary vectors to capture emergent behavior at the microscopic level. This bottom-up approach maintains physical interpretability while leveraging machine learning's pattern-recognition capabilities, offering a versatile framework for understanding transitions in soft matter, biological systems, and energy materials.
- 일반주제명
- Chemistry
- 일반주제명
- Physical chemistry
- 일반주제명
- Computer science
- 일반주제명
- Mechanics
- 일반주제명
- Chemical engineering
- 키워드
- Phase behavior
- 기타저자
- The University of Wisconsin - Madison Chemistry
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■035 ▼a(MiAaPQ)AAI32116491
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a540
■1001 ▼aJang, Inhyuk.
■24510▼aMachine Learning Meets Statistical Mechanics: Exploring Phase Behavior Through Local Affinity
■260 ▼a[Sl]▼bThe University of Wisconsin - Madison▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a135 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Yethiraj, Arun.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
■520 ▼aSudden changes in the states of matter, including phase and structural transitions, are fundamental phenomena observed across physical, chemical, and biological systems, yet their underlying mechanisms remain only partially understood-particularly in complex, multiscale, or farfrom-equilibrium environments. This work focuses on addressing these challenges through a combination of computational modeling and unsupervised machine learning (UML). In particular, I investigate dendrite formation in lithium metal batteries using stochastic dynamics simulations to explore morphological transitions and identify key factors influencing their growth. Beyond this, I aim to develop new methodologies for analyzing liquid-liquid phase separation (LLPS) in complex mixtures such as bio condensates and polyelectrolyte solutions, where conventional theories often fall short. By implementing UML, I construct data-driven phase diagrams and introduce a novel order parameter, local affinity, which encodes phase information as binary vectors to capture emergent behavior at the microscopic level. This bottom-up approach maintains physical interpretability while leveraging machine learning's pattern-recognition capabilities, offering a versatile framework for understanding transitions in soft matter, biological systems, and energy materials.
■590 ▼aSchool code: 0262.
■650 4▼aChemistry
■650 4▼aPhysical chemistry
■650 4▼aComputer science
■650 4▼aMechanics
■650 4▼aChemical engineering
■653 ▼aComputer simulation
■653 ▼aLiquid-liquid phase separation
■653 ▼aUnsupervised machine learning
■653 ▼aPhase behavior
■690 ▼a0485
■690 ▼a0984
■690 ▼a0346
■690 ▼a0542
■690 ▼a0494
■71020▼aThe University of Wisconsin - Madison▼bChemistry.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358445▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
