{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129398"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129398","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computational and machine learning tools for insights into conjugated materials","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Friday, David Mark"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Chemistry","degree_department":null,"school":null,"contributors":["Jackson, Nicholas E","Diao, Ying","Luthey-Schulten, Zaida","Sing, Charles"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-16","date_published":"2025-04-16","updated_at":"2026-07-22T22:25:05Z","subjects":["Conjugated Materials","Machine Learning","Molecular Dynamics","Coarse Grain","Quantum Mechanics","Conjugated Polyelectrolyte","Photostability"],"languages":["en","eng"],"rights":["© 2025 David Mark Friday"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129398","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jackson, Nicholas E","Diao, Ying","Luthey-Schulten, Zaida","Sing, Charles"]},{"key":"dc:creator","label":"Author","values":["Friday, David Mark"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-16","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"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":["Conjugated Materials","Machine Learning","Molecular Dynamics","Coarse Grain","Quantum Mechanics","Conjugated Polyelectrolyte","Photostability"]}]},{"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 David Mark Friday"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129398"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Friday, accepted the attached license on 2025-04-14 at 11:42.","The student, David Friday, submitted this Dissertation for approval on 2025-04-14 at 11:48.","This Dissertation was approved for publication on 2025-04-16 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21765 on 2025-10-19 at 18:18:14","Conjugated materials are a versatile class of electro-active organic materials with applications in energy, health, and computing. Central to improving their performance and enabling the implementation in devices is understanding their electronic properties (e.g. electronic mobility and photo-activity). These properties depend on quantum mechanical (QM) properties traditionally computed via DFT. However, DFT is unable to access the large length scales required to predict morphological-dependent properties (e.g. mobility), and the connection between QM-calculable properties and experimentally relevant molecular properties is not always clear. This work seeks to address these limitations by both developing new computational methods enabling prediction of morphologies and QM-informed electronic properties at experimentally relevant length scales, and methods to discover mechanistic insights from experimental campaigns via machine learning. The application of these methods elucidates novel mechanisms driving electronic mobility and photostability of conjugated materials, enabling their further optimization for future applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computational and machine learning tools for insights into conjugated materials"]}]}],"canonical_facts":{"dc:contributor":["Jackson, Nicholas E","Diao, Ying","Luthey-Schulten, Zaida","Sing, Charles"],"dc:creator":["Friday, David Mark"],"dc:date":["2025-04-16","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Friday, accepted the attached license on 2025-04-14 at 11:42.","The student, David Friday, submitted this Dissertation for approval on 2025-04-14 at 11:48.","This Dissertation was approved for publication on 2025-04-16 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21765 on 2025-10-19 at 18:18:14","Conjugated materials are a versatile class of electro-active organic materials with applications in energy, health, and computing. Central to improving their performance and enabling the implementation in devices is understanding their electronic properties (e.g. electronic mobility and photo-activity). These properties depend on quantum mechanical (QM) properties traditionally computed via DFT. However, DFT is unable to access the large length scales required to predict morphological-dependent properties (e.g. mobility), and the connection between QM-calculable properties and experimentally relevant molecular properties is not always clear. This work seeks to address these limitations by both developing new computational methods enabling prediction of morphologies and QM-informed electronic properties at experimentally relevant length scales, and methods to discover mechanistic insights from experimental campaigns via machine learning. The application of these methods elucidates novel mechanisms driving electronic mobility and photostability of conjugated materials, enabling their further optimization for future applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129398"],"dc:language":["en","eng"],"dc:rights":["© 2025 David Mark Friday"],"dc:subject":["Conjugated Materials","Machine Learning","Molecular Dynamics","Coarse Grain","Quantum Mechanics","Conjugated Polyelectrolyte","Photostability"],"dc:title":["Computational and machine learning tools for insights into conjugated materials"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Chemistry"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}