{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86673"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86673","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Designing Multicomponent Materials using Statistical Learning","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Dasgupta, Aparajita; 0000-0001-6598-319X"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Rajan, Krishna","Materials Design and Innovation"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:36:19Z","date_published":"2025-02-21T21:36:19Z","updated_at":"2026-07-27T19:05:34Z","subjects":["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/10477/86673","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rajan, Krishna","Materials Design and Innovation"]},{"key":"dc:creator","label":"Author","values":["Dasgupta, Aparajita; 0000-0001-6598-319X"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:36:19Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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/10477/86673"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","The design and deployment of multicomponent chemistries for novel materials remains a major challenge across all domains. The chief issue that arises is the massive search space that increases in complexity as the allowable number of combinatorial combinations increases. Furthermore, the appropriate domain representation for the resultant high dimensional data spaces that stem from such analyses remains a challenge. Thus, a robust strategy is required to introduce the appropriate representation and necessary computational tools and models that can survey the large number of combinations possible in an efficient manner and allow for understanding the crucial link between how modifications in chemistry affect properties for a given class of materials. In this dissertation we present an informatics approach to identify chemical discovery pathways for multicomponent materials using statistical analyses and high dimensional representations of multicomponent materials. We present a computational framework that enable us to navigate the issues of uncertainty within the existing high dimensionality that exists for such problems as well as develop new approaches for the efficient representation of the materials domain that encode the information required for the representation of such multicomponent chemistries rapidly and efficiently. Our work thus provides new techniques and results that allow for the universal exploration of the materials domain that is beyond the use cases presented here.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Designing Multicomponent Materials using Statistical Learning"]}]}],"canonical_facts":{"dc:contributor":["Rajan, Krishna","Materials Design and Innovation"],"dc:creator":["Dasgupta, Aparajita; 0000-0001-6598-319X"],"dc:date":["2025-02-21T21:36:19Z","2020"],"dc:description":["Ph.D.","The design and deployment of multicomponent chemistries for novel materials remains a major challenge across all domains. The chief issue that arises is the massive search space that increases in complexity as the allowable number of combinatorial combinations increases. Furthermore, the appropriate domain representation for the resultant high dimensional data spaces that stem from such analyses remains a challenge. Thus, a robust strategy is required to introduce the appropriate representation and necessary computational tools and models that can survey the large number of combinations possible in an efficient manner and allow for understanding the crucial link between how modifications in chemistry affect properties for a given class of materials. In this dissertation we present an informatics approach to identify chemical discovery pathways for multicomponent materials using statistical analyses and high dimensional representations of multicomponent materials. We present a computational framework that enable us to navigate the issues of uncertainty within the existing high dimensionality that exists for such problems as well as develop new approaches for the efficient representation of the materials domain that encode the information required for the representation of such multicomponent chemistries rapidly and efficiently. Our work thus provides new techniques and results that allow for the universal exploration of the materials domain that is beyond the use cases presented here.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86673"],"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":["materials science"],"dc:title":["Designing Multicomponent Materials using Statistical Learning"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:34Z"}