{"id":{"repo_id":"ku","oai_identifier":"oai:kuscholarworks.ku.edu:1808/38429"},"canonical_url":"https://search.dev.ndltd.org/etd/ku/oai:kuscholarworks.ku.edu:1808/38429","repository":{"repo_id":"ku","name":"University of Kansas","base_url":"https://kuscholarworks.ku.edu/server/oai/request"},"display":{"title":"A Study Of Electrocatalysis In The Oxygen Reduction Reaction Using OpenCatalyst, a Deep-Learning Platform.","abstract":"The efficiency of Proton Exchange Membrane Fuel Cells (PEMFCs) is fundamentally influenced by the Oxygen Reduction Reaction (ORR) at the cathode. This dissertation presents a comprehensive investigation into the ORR kinetics and mechanism, with a specific focus on the electrocatalytic properties of bimetallic catalysts—particularly palladiumgold alloys—and their potential to exceed the performance of traditional platinum-based systems. By utilizing Machine Learning (ML) methods as an alternative to Density Functional Theory (DFT), this study demonstrates that ML can predict adsorption energies and relaxation pathways with remarkable accuracy, closely approximating DFT results while significantly reducing computational costs. The research leverages the resources of the Open Catalyst Project (OCP) to systematically screen and optimize catalysts, enabling rapid and high-throughput evaluation of catalytic efficiency. This ML-driven approach provides a transformative framework for catalyst discovery, facilitating the exploration of dual-site catalytic mechanisms intrinsic to bimetallic systems, which offer potential pathways to address limitations associated with conventional single-metal catalysts.","abstract_html":"The efficiency of Proton Exchange Membrane Fuel Cells (PEMFCs) is fundamentally influenced by the Oxygen Reduction Reaction (ORR) at the cathode. This dissertation presents a comprehensive investigation into the ORR kinetics and mechanism, with a specific focus on the electrocatalytic properties of bimetallic catalysts—particularly palladiumgold alloys—and their potential to exceed the performance of traditional platinum-based systems. By utilizing Machine Learning (ML) methods as an alternative to Density Functional Theory (DFT), this study demonstrates that ML can predict adsorption energies and relaxation pathways with remarkable accuracy, closely approximating DFT results while significantly reducing computational costs. The research leverages the resources of the Open Catalyst Project (OCP) to systematically screen and optimize catalysts, enabling rapid and high-throughput evaluation of catalytic efficiency. This ML-driven approach provides a transformative framework for catalyst discovery, facilitating the exploration of dual-site catalytic mechanisms intrinsic to bimetallic systems, which offer potential pathways to address limitations associated with conventional single-metal catalysts.","abstract_has_math":false,"creators":["Gnanasekar Sahaya Muzhumathi, Anto Felix Sotvik"],"institution":"University of Kansas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Leonard, Kevin"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01","date_published":"2024-01-01","updated_at":"2026-07-24T02:45:42Z","subjects":["Chemical engineering"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["http://dissertations.umi.com/ku:19915"],"render_values":[{"text":"http://dissertations.umi.com/ku:19915","href":"http://dissertations.umi.com/ku:19915","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1808/38429","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Leonard, Kevin"]},{"key":"dc:creator","label":"Author","values":["Gnanasekar Sahaya Muzhumathi, Anto Felix Sotvik"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-24T02:12:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-24T02:12:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-01-01"]},{"key":"dc:publisher","label":"Institution","values":["University of Kansas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Chemical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["http://dissertations.umi.com/ku:19915"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1808/38429"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The efficiency of Proton Exchange Membrane Fuel Cells (PEMFCs) is fundamentally influenced by the Oxygen Reduction Reaction (ORR) at the cathode. This dissertation presents a comprehensive investigation into the ORR kinetics and mechanism, with a specific focus on the electrocatalytic properties of bimetallic catalysts—particularly palladiumgold alloys—and their potential to exceed the performance of traditional platinum-based systems. By utilizing Machine Learning (ML) methods as an alternative to Density Functional Theory (DFT), this study demonstrates that ML can predict adsorption energies and relaxation pathways with remarkable accuracy, closely approximating DFT results while significantly reducing computational costs. The research leverages the resources of the Open Catalyst Project (OCP) to systematically screen and optimize catalysts, enabling rapid and high-throughput evaluation of catalytic efficiency. This ML-driven approach provides a transformative framework for catalyst discovery, facilitating the exploration of dual-site catalytic mechanisms intrinsic to bimetallic systems, which offer potential pathways to address limitations associated with conventional single-metal catalysts."]},{"key":"dc:title","label":"Title","values":["A Study Of Electrocatalysis In The Oxygen Reduction Reaction Using OpenCatalyst, a Deep-Learning Platform."]}]}],"canonical_facts":{"dc:contributor.advisor":["Leonard, Kevin"],"dc:creator":["Gnanasekar Sahaya Muzhumathi, Anto Felix Sotvik"],"dc:date.accessioned":["2026-04-24T02:12:43Z"],"dc:date.available":["2026-04-24T02:12:43Z"],"dc:date.issued":["2024-01-01"],"dc:description.abstract":["The efficiency of Proton Exchange Membrane Fuel Cells (PEMFCs) is fundamentally influenced by the Oxygen Reduction Reaction (ORR) at the cathode. This dissertation presents a comprehensive investigation into the ORR kinetics and mechanism, with a specific focus on the electrocatalytic properties of bimetallic catalysts—particularly palladiumgold alloys—and their potential to exceed the performance of traditional platinum-based systems. By utilizing Machine Learning (ML) methods as an alternative to Density Functional Theory (DFT), this study demonstrates that ML can predict adsorption energies and relaxation pathways with remarkable accuracy, closely approximating DFT results while significantly reducing computational costs. The research leverages the resources of the Open Catalyst Project (OCP) to systematically screen and optimize catalysts, enabling rapid and high-throughput evaluation of catalytic efficiency. This ML-driven approach provides a transformative framework for catalyst discovery, facilitating the exploration of dual-site catalytic mechanisms intrinsic to bimetallic systems, which offer potential pathways to address limitations associated with conventional single-metal catalysts."],"dc:identifier.other":["http://dissertations.umi.com/ku:19915"],"dc:identifier.uri":["https://hdl.handle.net/1808/38429"],"dc:language.iso":["en"],"dc:publisher":["University of Kansas"],"dc:subject":["Chemical engineering"],"dc:title":["A Study Of Electrocatalysis In The Oxygen Reduction Reaction Using OpenCatalyst, a Deep-Learning Platform."],"dc:type":["Thesis"]},"updated_at":"2026-07-24T02:45:42Z"}