{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101505"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101505","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Nonlinear machine learning of macromolecular folding and self-assembly","abstract":"High performance computation and sophisticated machine learning algorithms have emerged as new tools for studying biological, physical and chemical systems at the atomistic scale. In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the macromolecular folding and assembly. In the first aspect, I employ molecular simulation and non-linear manifold learning to explore the dynamics and configuration of linear and ring polymers. Integrating statistical mechanics with dynamical systems theory, I establish a means to determine single molecule folding funnels from univariate time series in experimentally accessible observables. In the second aspect, I utilize coarse grained molecular simulation to explore the self-assembly of hundreds of asphaltene molecules over micro-second time scales to reveal the aggregation phase behavior as a function of temperature, pressure and solvent conditions. I then employ graph matching and non-linear manifold learning to obtain asphaltene folding and assembly free energy landscapes. This thesis establishes new fundamental understanding of the folding and assembly of macromolecules, builds connections between computer simulation and experimental measurements, and provides new routes to the rational design of functional molecular materials.","abstract_html":"High performance computation and sophisticated machine learning algorithms have emerged as new tools for studying biological, physical and chemical systems at the atomistic scale. In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the macromolecular folding and assembly. In the first aspect, I employ molecular simulation and non-linear manifold learning to explore the dynamics and configuration of linear and ring polymers. Integrating statistical mechanics with dynamical systems theory, I establish a means to determine single molecule folding funnels from univariate time series in experimentally accessible observables. In the second aspect, I utilize coarse grained molecular simulation to explore the self-assembly of hundreds of asphaltene molecules over micro-second time scales to reveal the aggregation phase behavior as a function of temperature, pressure and solvent conditions. I then employ graph matching and non-linear manifold learning to obtain asphaltene folding and assembly free energy landscapes. This thesis establishes new fundamental understanding of the folding and assembly of macromolecules, builds connections between computer simulation and experimental measurements, and provides new routes to the rational design of functional molecular materials.","abstract_has_math":false,"creators":["Wang, Jiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Ferguson, Andrew","Kuehn, Seppe","Tajkhorshid, Emad","DeVille, Lee"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:17:32Z","date_published":"2018-09-27T16:17:32Z","updated_at":"2026-07-22T22:24:40Z","subjects":["molecular simulation, protein folding, self-assembly, manifold learning, machine learning, asphaltene aggregation, ring molecule, dynamical systems theory"],"languages":["en"],"rights":["Copyright 2018 Jiang Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101505","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ferguson, Andrew","Kuehn, Seppe","Tajkhorshid, Emad","DeVille, Lee"]},{"key":"dc:creator","label":"Author","values":["Wang, Jiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:17:32Z","2018-06-27","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["molecular simulation, protein folding, self-assembly, manifold learning, machine learning, asphaltene aggregation, ring molecule, dynamical systems theory"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Jiang Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101505"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["High performance computation and sophisticated machine learning algorithms have emerged as new tools for studying biological, physical and chemical systems at the atomistic scale. In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the macromolecular folding and assembly. In the first aspect, I employ molecular simulation and non-linear manifold learning to explore the dynamics and configuration of linear and ring polymers. Integrating statistical mechanics with dynamical systems theory, I establish a means to determine single molecule folding funnels from univariate time series in experimentally accessible observables. In the second aspect, I utilize coarse grained molecular simulation to explore the self-assembly of hundreds of asphaltene molecules over micro-second time scales to reveal the aggregation phase behavior as a function of temperature, pressure and solvent conditions. I then employ graph matching and non-linear manifold learning to obtain asphaltene folding and assembly free energy landscapes. This thesis establishes new fundamental understanding of the folding and assembly of macromolecules, builds connections between computer simulation and experimental measurements, and provides new routes to the rational design of functional molecular materials.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Jiang Wang, accepted the attached license on 2018-06-26 at 12:18.","The student, Jiang Wang, submitted this Dissertation for approval on 2018-06-26 at 12:31.","This Dissertation was approved for publication on 2018-06-27 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12671 on 2018-09-27 at 10:45:43","Made available in DSpace on 2018-09-27T16:17:32Z (GMT). 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In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the macromolecular folding and assembly. In the first aspect, I employ molecular simulation and non-linear manifold learning to explore the dynamics and configuration of linear and ring polymers. Integrating statistical mechanics with dynamical systems theory, I establish a means to determine single molecule folding funnels from univariate time series in experimentally accessible observables. In the second aspect, I utilize coarse grained molecular simulation to explore the self-assembly of hundreds of asphaltene molecules over micro-second time scales to reveal the aggregation phase behavior as a function of temperature, pressure and solvent conditions. I then employ graph matching and non-linear manifold learning to obtain asphaltene folding and assembly free energy landscapes. This thesis establishes new fundamental understanding of the folding and assembly of macromolecules, builds connections between computer simulation and experimental measurements, and provides new routes to the rational design of functional molecular materials.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Jiang Wang, accepted the attached license on 2018-06-26 at 12:18.","The student, Jiang Wang, submitted this Dissertation for approval on 2018-06-26 at 12:31.","This Dissertation was approved for publication on 2018-06-27 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12671 on 2018-09-27 at 10:45:43","Made available in DSpace on 2018-09-27T16:17:32Z (GMT). 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