| In condensed matter physics, researchers study the physical properties of condensed phases of matter, theoretically or experimentally. The fundamentally appealing topic in this research area is how to classify phases of matter and identify phase transitions between them.;Different from traditional theoretical or experimental approaches, which relies on either complicated mathematical formulation or equally complex experimental equipment, Monte Carlo based stochastic methods, which are often treated as ``computer experiments", introduce a relatively ``cheap" but effective approach to study phases and phase transitions. In this dissertation, we employ the classical Monte Carlo simulation, which utilizes the Metropolis algorithm to evolve system configurations, and also the determinant quantum Monte Carlo simulation to study phases and phase transitions of model Hamiltonians, such as the Hubbard model, and the periodic Anderson model (PAM).;In the 21st century, data driven machine learning techniques have proven to be an another research ``engine" for detecting phases and phase transitions. In this dissertation, I explore potential usages of unsupervised machine learning techniques in phase transition. Specifically, I leverage the principal component analysis (PCA) to extract internal structures, which are fully reflected in leading principal components, of Monte Carlo generated configurations, and then quantify obtained principal components to distinguish phases and phase transitions. This technique is applied to study model Hamiltonians, such as the Ising model, the XY model, the Hubbard model and the PAM.;The exact organization of this dissertation is as follows: In chapter 1, I first introduce basic concepts of phase transitions and related model Hamiltonians. In chapter 2, I talk about a variety of methodologies utilized. In chapter 3, I present studies of phase transitions in a spin-fermion model. In chapter 4, I explore phase diagrams of the PAM coupled with an additional layer of metal. In chapter 5 and 6, I discuss how to apply machine learning techniques, especially PCA, to distinguish phases and detect phase transitions in classical and quantum model Hamiltonians. In chapter 7, I summarize previous chapters and discuss potential future directions. |
