Abstract

Topological quantum materials have sparked intense research due to their robust physical properties and technological promise. While non-interacting topological phases are well understood, the topic becomes more complex when strong correlations are introduced --- often rendering conventional theoretical tools insufficient. Faced with a growing number of candidate materials and powerful experimental toolkit enabled by university and national laboratory collaboration, a critical question emerges: which materials warrant intensive study, and when do strong correlations demand a departure from standard approaches? My dissertation address this challenge by developing an experimentally driven strategy for identifying and characterizing correlated topological materials. Focusing on uranium-based single crystals, this work combines machine learning technologies --- including Random Forest classification and Bayesian optimization --- with electron transport probes, such as inverted resistance measurements for detecting surface states and quantum oscillation measurements to resolve the electronic structure. These tools are deployed not only to understand emergent topological behavior in 5$f$-electron systems, but also define a framework for selecting high-impact materials that advance our knowledge of correlated topology.

Committee Chair

Sheng Ran

Committee Members

Erik Henriksen; Karthik Ramanathan; Rohan Mishra; Zohar Nussinov

Degree

Doctor of Philosophy (PhD)

Author's Department

Physics

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

5-5-2025

Language

English (en)

Author's ORCID

https://orcid.org/0000-0002-3661-7585

Available for download on Thursday, April 22, 2027

Included in

Physics Commons

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