Date of Award
Spring 1-1-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Applied Physics
First Advisor
Ismail-Beigi, Sohrab
Abstract
Strongly correlated oxides, including high-temperature superconductors such as cuprates and nickelates, exhibit complex quantum phenomena that can challenge conventional electronic structure theories. This thesis develops and applies advanced computational methods to study these systems, aiming to provide a comprehensive first-principles description of their structural, spin, and electronic properties. First, a novel cluster slave-particle method is introduced to improve the treatment of electron correlations in extended Hubbard models, capturing strong correlation effects with high computational efficiency and benchmarking well against established many-body techniques. This method is then combined with density functional theory calculations and applied to cuprates, where we demonstrate how structural distortions significantly impact interlayer couplings, charge-transfer gaps, spin correlations, and therefore electron pairing. Next, interlayer coupling mechanisms in cuprates are systematically investigated, leading to analytical expressions for estimating hopping strengths and effective interlayer couplings based on crystal structure. These frameworks further enable us to assist our experimental collaborators in understanding the effects of Praseodymium doping in YBa$_2$Cu$_3$O$_{7-x}$. Extending these insights to nickelates, the thesis explores the spin fluctuation effects in infinite-layer nickelates and estimates exchange interactions that drive unique magnetoresistive effects. By integrating many-body theory, first-principles calculations, and experimental data, this work advances the understanding of correlated electron systems and provides guidance for the design of new superconducting materials.
Recommended Citation
Jin, Zheting, "Development and Application of New Method and Platform for Correlated Oxides" (2025). Yale Graduate School of Arts and Sciences Dissertations. 1664.
https://elischolar.library.yale.edu/gsas_dissertations/1664