{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129674"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129674","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven acceleration of molecular dynamics simulation for nanoscale fluids with coarse-grained and surrogate modeling","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Jeong, Jinu"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Aluru, Narayana R","van der Zande, Arend","Salapaka, Srinivasa M","Sing, Charles E"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-07","date_published":"2025-04-07","updated_at":"2026-07-22T22:25:05Z","subjects":["Data-Driven Physics Modeling","Machine Learning","Neural Network","Molecular Dynamics Simulation","Coarse-grained modeling","Generalized Langevin Equation","Stochastic Differential Equation","Nanofluidics"],"languages":["en","eng"],"rights":["© 2025 Jinu Jeong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129674","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aluru, Narayana R","van der Zande, Arend","Salapaka, Srinivasa M","Sing, Charles E"]},{"key":"dc:creator","label":"Author","values":["Jeong, Jinu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-07","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data-Driven Physics Modeling","Machine Learning","Neural Network","Molecular Dynamics Simulation","Coarse-grained modeling","Generalized Langevin Equation","Stochastic Differential Equation","Nanofluidics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025 Jinu Jeong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129674"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Jinu Jeong, accepted the attached license on 2025-03-23 at 18:07.","The student, Jinu Jeong, submitted this Dissertation for approval on 2025-03-26 at 03:00.","This Dissertation was approved for publication on 2025-04-07 at 12:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21688 on 2025-10-19 at 19:52:32","Nanofluidics, a rapidly growing field, focuses on the transport phenomena of fluids and ions in nanopores and membranes, presenting significant potential for applications such as water desalination, energy storage, and biomedical devices. However, as the scale of these systems decreases, experimental methods face challenges in obtaining atomic-scale details and controlling the environment precisely. High-resolution atomistic simulations provide a viable alternative, offering detailed insights into molecular behavior that are difficult to achieve experimentally. However, the computational cost of these simulations often limits their applicability to small systems and short timescales. This thesis addresses these challenges by employing advanced coarse-grained modeling and surrogate modeling techniques to efficiently simulate high-resolution physics. Coarse-grained modeling simplifies molecular systems by reducing the number of degrees of freedom, enabling the study of larger systems over longer timescales while retaining essential physical properties. However, traditional CG models have significant drawbacks, such as requiring multiple iterations to accurately capture system behavior and often producing diffusion coefficients and velocity-autocorrelation functions (VACFs) that deviate from all-atom (AA) results. To overcome these limitations, we utilized machine learning to parameterize the CG models, introduced perturbations to the free energy landscape, and employed Generalized Langevin Equation (GLE) parameterization to ensure dynamic properties are accurately represented. These enhancements address the inefficiencies and inaccuracies of traditional CG models, providing a more reliable and efficient approach to simulating molecular dynamics. Surrogate modeling aims to create efficient approximations of complex molecular interactions. We employ machine learning techniques to develop surrogate models for various molecular systems, deriving force fields from quantum simulation results. This approach bridges the gap between quantum simulations and continuum scales, creating a multiscale framework capable of capturing the detailed molecular interactions and chemical reactions involved in transport phenomena. Our research demonstrates that surrogate modeling can make quantum-accurate simulations more affordable and practical for large-scale applications. By combining advanced CG modeling and surrogate modeling, this thesis offers a framework for significantly speeding up simulations while maintaining high accuracy. This enables the exploration of large time and length scales that were previously inaccessible with traditional methods because of the computational burden. Our approach also provides valuable insights into molecular transport phenomena and opens up new possibilities for the design and optimization of nanofluidic devices and membranes across a wide range of applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven acceleration of molecular dynamics simulation for nanoscale fluids with coarse-grained and surrogate modeling"]}]}],"canonical_facts":{"dc:contributor":["Aluru, Narayana R","van der Zande, Arend","Salapaka, Srinivasa M","Sing, Charles E"],"dc:creator":["Jeong, Jinu"],"dc:date":["2025-04-07","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Jinu Jeong, accepted the attached license on 2025-03-23 at 18:07.","The student, Jinu Jeong, submitted this Dissertation for approval on 2025-03-26 at 03:00.","This Dissertation was approved for publication on 2025-04-07 at 12:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21688 on 2025-10-19 at 19:52:32","Nanofluidics, a rapidly growing field, focuses on the transport phenomena of fluids and ions in nanopores and membranes, presenting significant potential for applications such as water desalination, energy storage, and biomedical devices. However, as the scale of these systems decreases, experimental methods face challenges in obtaining atomic-scale details and controlling the environment precisely. High-resolution atomistic simulations provide a viable alternative, offering detailed insights into molecular behavior that are difficult to achieve experimentally. However, the computational cost of these simulations often limits their applicability to small systems and short timescales. This thesis addresses these challenges by employing advanced coarse-grained modeling and surrogate modeling techniques to efficiently simulate high-resolution physics. Coarse-grained modeling simplifies molecular systems by reducing the number of degrees of freedom, enabling the study of larger systems over longer timescales while retaining essential physical properties. However, traditional CG models have significant drawbacks, such as requiring multiple iterations to accurately capture system behavior and often producing diffusion coefficients and velocity-autocorrelation functions (VACFs) that deviate from all-atom (AA) results. To overcome these limitations, we utilized machine learning to parameterize the CG models, introduced perturbations to the free energy landscape, and employed Generalized Langevin Equation (GLE) parameterization to ensure dynamic properties are accurately represented. These enhancements address the inefficiencies and inaccuracies of traditional CG models, providing a more reliable and efficient approach to simulating molecular dynamics. Surrogate modeling aims to create efficient approximations of complex molecular interactions. We employ machine learning techniques to develop surrogate models for various molecular systems, deriving force fields from quantum simulation results. This approach bridges the gap between quantum simulations and continuum scales, creating a multiscale framework capable of capturing the detailed molecular interactions and chemical reactions involved in transport phenomena. Our research demonstrates that surrogate modeling can make quantum-accurate simulations more affordable and practical for large-scale applications. By combining advanced CG modeling and surrogate modeling, this thesis offers a framework for significantly speeding up simulations while maintaining high accuracy. This enables the exploration of large time and length scales that were previously inaccessible with traditional methods because of the computational burden. Our approach also provides valuable insights into molecular transport phenomena and opens up new possibilities for the design and optimization of nanofluidic devices and membranes across a wide range of applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129674"],"dc:language":["en","eng"],"dc:rights":["© 2025 Jinu Jeong"],"dc:subject":["Data-Driven Physics Modeling","Machine Learning","Neural Network","Molecular Dynamics Simulation","Coarse-grained modeling","Generalized Langevin Equation","Stochastic Differential Equation","Nanofluidics"],"dc:title":["Data-driven acceleration of molecular dynamics simulation for nanoscale fluids with coarse-grained and surrogate modeling"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}