{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/77967"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/77967","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Afzal, Mohammad Atif; 0000-0001-8261-2024"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hachmann, Johannes","Chemical and Biological Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-06-28T20:31:52Z","date_published":"2018-06-28T20:31:52Z","updated_at":"2026-07-27T19:05:05Z","subjects":["computational chemistry","molecular chemistry","materials science"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/77967","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hachmann, Johannes","Chemical and Biological Engineering"]},{"key":"dc:creator","label":"Author","values":["Afzal, Mohammad Atif; 0000-0001-8261-2024"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-06-28T20:31:52Z","2018","2018-05-11 08:43:51"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computational chemistry","molecular chemistry","materials science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/77967"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","This dissertation is concerned with the application of materials discovery framework developed in our group to discover high-refractive-index polymers. Development and application of the framework includes four key parts. In the first part, we present a method to accurately predict the refractive index (RI) of polymers using a combination of first-principles and data modeling. We validated the model with experimental RI values of polymers (Chapter 2). We further benchmark our results using different model chemistries to optimize the tradeoff between the accuracy and computation time (Chapter 3). The second part covers the development of a molecular library generator (ChemLG) and a virtual high-throughput screening (ChemHTPS) infrastructure. We demonstrate the applicability of these software suites by providing examples (Chapter 4). In the third part, we apply ChemLG and ChemHTPS to generate a library of polyimides and compute their RI values, respectively. Using the data generated in this work, we identify structure-property relationships via hypergeometric distribution analysis (Chapter 5). Finally, we present the application of machine learning to accelerate the process of property prediction. We construct efficient machine learning models to accurately predict the packing density, polarizability, and RI values of organic molecules and characterize them on a massive scale (Chapter 6)."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications"]}]}],"canonical_facts":{"dc:contributor":["Hachmann, Johannes","Chemical and Biological Engineering"],"dc:creator":["Afzal, Mohammad Atif; 0000-0001-8261-2024"],"dc:date":["2018-06-28T20:31:52Z","2018","2018-05-11 08:43:51"],"dc:description":["Ph.D.","This dissertation is concerned with the application of materials discovery framework developed in our group to discover high-refractive-index polymers. Development and application of the framework includes four key parts. In the first part, we present a method to accurately predict the refractive index (RI) of polymers using a combination of first-principles and data modeling. We validated the model with experimental RI values of polymers (Chapter 2). We further benchmark our results using different model chemistries to optimize the tradeoff between the accuracy and computation time (Chapter 3). The second part covers the development of a molecular library generator (ChemLG) and a virtual high-throughput screening (ChemHTPS) infrastructure. We demonstrate the applicability of these software suites by providing examples (Chapter 4). In the third part, we apply ChemLG and ChemHTPS to generate a library of polyimides and compute their RI values, respectively. Using the data generated in this work, we identify structure-property relationships via hypergeometric distribution analysis (Chapter 5). Finally, we present the application of machine learning to accelerate the process of property prediction. We construct efficient machine learning models to accurately predict the packing density, polarizability, and RI values of organic molecules and characterize them on a massive scale (Chapter 6)."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/77967"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computational chemistry","molecular chemistry","materials science"],"dc:title":["From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications"],"dc:type":["Dissertation","Text"]},"updated_at":"2026-07-27T19:05:05Z"}