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Publication Date
2023-5
First Advisor
Casey Berger
Document Type
Honors Project
Degree Name
Bachelor of Arts
Department
Physics
Keywords
physics, computational physics, quantum physics, machine learning, Monte Carlo method, Ising model pyrochlore iridate
Abstract
Pyrochlore iridates are a class of materials that experience a metal to insulator phase transition in response to temperature or pressure. These materials are of particular interest due to their applications for superconductivity and other areas of physics. This thesis investigates methods of modeling and identifying phase transitions. The statistical nature of phase transitions lends itself well to computational study, however there are limitations such as the sign problem. Due to the complexity of pyrochlore iridates, the Ising model was first created using Monte Carlo methods to explore simple phase transitions dependent on temperature. To categorize these phase transitions, this project used two types of machine learning: supervised and unsupervised. The methods for creating the Ising model were applied to a toy model for pyrochlore iridates. With this toy model, the feasibility of using neural networks to identify phases, which specific focus on the Weyl semimetal phase, for pyrochlore iridates was tested.
Rights
©2023 Elizabeth Morningstar. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Language
English
Recommended Citation
Morningstar, Elizabeth, "Investigating Weyl Semimetal and Magnetized Phases in Quantum Materials Using Stochastic and Machine Learning Methods" (2023). Honors Project, Smith College, Northampton, MA.
https://scholarworks.smith.edu/theses/2576
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Comments
85 pages: color illustrations, charts. Includes bibliographical references (pages 82-85).