{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108655"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108655","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning of molecular conformations, kinetics and beyond","abstract":"Machine learning has been playing an increasingly important role in many fields of computational physics, including molecular simulation. In this thesis, I report my work on machine learning method developments for molecular simulation, and their applications on learning conformations and kinetics for molecular systems. First, I present a deep-learning based accelerated sampling framework termed “Molecular Enhanced Sampling with Autoencoders” (MESA) that utilizes high-variance collective variables (CVs) to guide sampling. By applying the framework on some molecular systems, I show its efficiency for exploring configuration space, and discuss several aspects for improvements. Second, I build a deep-learning based model termed “State-free Reversible VAMPnets” (SRVs) to learn slow CVs that govern the dominant kinetics of the system. By comparing SRVs with the existing kernel based method, I show that SRVs are more accurate, less sensitive to feature selection and feature scaling, and more computationally efficient. Also, extensive mathematical analysis provides theoretical guarantees for the correctness of the SRV model. Combined with Markov state models (MSMs), I show that CVs discovered by SRVs serve as excellent basis for constructing MSMs that enables high-resolution kinetics analysis, which opens the door to applications for many important physical processes. In sum, this thesis establishes new machine learning methods for learning molecular conformations, kinetics and other physical properties, builds connections between theoretical developments and computational applications, and provides new insights for both machine learning and computational physics communities.","abstract_html":"Machine learning has been playing an increasingly important role in many fields of computational physics, including molecular simulation. In this thesis, I report my work on machine learning method developments for molecular simulation, and their applications on learning conformations and kinetics for molecular systems. First, I present a deep-learning based accelerated sampling framework termed “Molecular Enhanced Sampling with Autoencoders” (MESA) that utilizes high-variance collective variables (CVs) to guide sampling. By applying the framework on some molecular systems, I show its efficiency for exploring configuration space, and discuss several aspects for improvements. Second, I build a deep-learning based model termed “State-free Reversible VAMPnets” (SRVs) to learn slow CVs that govern the dominant kinetics of the system. By comparing SRVs with the existing kernel based method, I show that SRVs are more accurate, less sensitive to feature selection and feature scaling, and more computationally efficient. Also, extensive mathematical analysis provides theoretical guarantees for the correctness of the SRV model. Combined with Markov state models (MSMs), I show that CVs discovered by SRVs serve as excellent basis for constructing MSMs that enables high-resolution kinetics analysis, which opens the door to applications for many important physical processes. In sum, this thesis establishes new machine learning methods for learning molecular conformations, kinetics and other physical properties, builds connections between theoretical developments and computational applications, and provides new insights for both machine learning and computational physics communities.","abstract_has_math":false,"creators":["Chen, Wei"],"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 L","Kuehn, Seppe","Cooper, Lance","Shukla, Diwakar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T22:48:04Z","date_published":"2020-10-07T22:48:04Z","updated_at":"2026-07-22T22:24:48Z","subjects":["machine learning","molecular simulation","deep learning","autoencoders","enhanced sampling"],"languages":["en"],"rights":["Copyright 2020 Wei Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108655","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ferguson, Andrew L","Kuehn, Seppe","Cooper, Lance","Shukla, Diwakar"]},{"key":"dc:creator","label":"Author","values":["Chen, Wei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T22:48:04Z","2022-10-07T22:50:13Z","2020-05-29","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["machine learning","molecular simulation","deep learning","autoencoders","enhanced sampling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Wei Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108655"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Machine learning has been playing an increasingly important role in many fields of computational physics, including molecular simulation. In this thesis, I report my work on machine learning method developments for molecular simulation, and their applications on learning conformations and kinetics for molecular systems. First, I present a deep-learning based accelerated sampling framework termed “Molecular Enhanced Sampling with Autoencoders” (MESA) that utilizes high-variance collective variables (CVs) to guide sampling. By applying the framework on some molecular systems, I show its efficiency for exploring configuration space, and discuss several aspects for improvements. Second, I build a deep-learning based model termed “State-free Reversible VAMPnets” (SRVs) to learn slow CVs that govern the dominant kinetics of the system. By comparing SRVs with the existing kernel based method, I show that SRVs are more accurate, less sensitive to feature selection and feature scaling, and more computationally efficient. Also, extensive mathematical analysis provides theoretical guarantees for the correctness of the SRV model. Combined with Markov state models (MSMs), I show that CVs discovered by SRVs serve as excellent basis for constructing MSMs that enables high-resolution kinetics analysis, which opens the door to applications for many important physical processes. In sum, this thesis establishes new machine learning methods for learning molecular conformations, kinetics and other physical properties, builds connections between theoretical developments and computational applications, and provides new insights for both machine learning and computational physics communities.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-08-01","The student, Wei Chen, accepted the attached license on 2020-05-26 at 14:32.","The student, Wei Chen, submitted this Dissertation for approval on 2020-05-26 at 14:44.","This Dissertation was approved for publication on 2020-05-29 at 14:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15419 on 2020-10-02 at 15:48:53","Made available in DSpace on 2020-10-07T22:48:04Z (GMT). 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In this thesis, I report my work on machine learning method developments for molecular simulation, and their applications on learning conformations and kinetics for molecular systems. First, I present a deep-learning based accelerated sampling framework termed “Molecular Enhanced Sampling with Autoencoders” (MESA) that utilizes high-variance collective variables (CVs) to guide sampling. By applying the framework on some molecular systems, I show its efficiency for exploring configuration space, and discuss several aspects for improvements. Second, I build a deep-learning based model termed “State-free Reversible VAMPnets” (SRVs) to learn slow CVs that govern the dominant kinetics of the system. By comparing SRVs with the existing kernel based method, I show that SRVs are more accurate, less sensitive to feature selection and feature scaling, and more computationally efficient. Also, extensive mathematical analysis provides theoretical guarantees for the correctness of the SRV model. Combined with Markov state models (MSMs), I show that CVs discovered by SRVs serve as excellent basis for constructing MSMs that enables high-resolution kinetics analysis, which opens the door to applications for many important physical processes. In sum, this thesis establishes new machine learning methods for learning molecular conformations, kinetics and other physical properties, builds connections between theoretical developments and computational applications, and provides new insights for both machine learning and computational physics communities.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-08-01","The student, Wei Chen, accepted the attached license on 2020-05-26 at 14:32.","The student, Wei Chen, submitted this Dissertation for approval on 2020-05-26 at 14:44.","This Dissertation was approved for publication on 2020-05-29 at 14:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15419 on 2020-10-02 at 15:48:53","Made available in DSpace on 2020-10-07T22:48:04Z (GMT). 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