A Study Of Electrocatalysis In The Oxygen Reduction Reaction Using OpenCatalyst, a Deep-Learning Platform.

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.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2024

Author dc:creator

• Gnanasekar Sahaya Muzhumathi, Anto Felix Sotvik

Advisor dc:contributor.advisor

• Leonard, Kevin

• Chemical engineering

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:19915

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/38429