{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124051"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124051","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Development of electronic structure and kinetics methods for the rational design of electrocatalysts","abstract":"Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET's ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites.","abstract_html":"Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET's ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites.","abstract_has_math":false,"creators":["Ricke, Nathan Darrell Peterson."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Chemistry","school":null,"contributors":[],"advisors":["Troy Van Voorhis."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:14Z","subjects":["Chemistry."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/124051","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Troy Van Voorhis."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Chemistry","Chem"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/124051"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 79-87)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET's ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Development of electronic structure and kinetics methods for the rational design of electrocatalysts"]}]}],"canonical_facts":{"dc:contributor.advisor":["Troy Van Voorhis."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Chemistry","Chem"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Chemistry."],"dc:creator":["Ricke, Nathan Darrell Peterson."],"dc:date.accessioned":["2020-03-09T18:51:08Z"],"dc:date.available":["2020-03-09T18:51:08Z"],"dc:date.issued":["2019"],"dc:description":["Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 79-87)."],"dc:description.abstract":["Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET's ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites."],"dc:description.degree":["Ph. D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/124051"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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