Acceptor doped barium zirconate has shown great potential for proton conduction in fuel cell applications. Experimentally, the different dopant ions and different oxygen vacancy concentrations lead to significantly different proton conductivities. My group of undergraduates and I have pioneered the use of graph theory and probabilistic approaches to understand proton conduction in perovskites with a variety of defects. While obtaining the data sets needed for these approaches, students develop an understanding of how defects change the potential energy landscape for ionic conduction. Students interested in method development take inspiration from network modeling and graph theory to assist in the use and development of new algorithms. While graph theory searches provide long-range periodic pathways, kinetic Monte Carlo (kMC) creates a time-based trajectory which reveals trapping and periodic escapes to the long-range periodic pathways. Our own time-based centrality measures highlight traps and long-range pathways in a single image. This system of tools helps us see how defects facilitate proton conduction pathways while simultaneously helping students find their areas of strength facilitating their own future pathways.

<b>Award Address</b> (ACS Award for Research at an Undergraduate Institution sponsored by Research Corporation for Science Advancement). Facilitating proton conduction and undergraduate pathways

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Award Address (ACS Award for Research at an Undergraduate Institution sponsored by Research Corporation for Science Advancement). Facilitating proton conduction and undergraduate pathways