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Optical and magneto-optical properties of topological and Dirac materials
Tikuišis, Kristupas Kazimieras ; Veis, Martin (advisor) ; Slobodeniuk, Artur (referee) ; Erlingsson, Sigurdur Ingi (referee)
Kristupas Kazimieras Tikuišis, Department of Optoelectronics and Magneto- optics, Institute of Physics of Charles University Doctoral thesis: Optical and magneto-optical properties of topological and Dirac materials Supervisor: RNDr. Martin Veis, Ph.D., Department of Optoelectronics and Magneto-optics Advisers: RNDr. Roman Antoš, Ph.D., Department of Optoelectronics and Magneto-optics; Milan Orlita, Ph.D., National Laboratory of High Magnetic Fields LNCMI - Grenoble, CNRS (France) Abstract: Dirac materials exhibit unusual properties that attract interest both for practi- cal applications and fundamental reseach of exotic and relativistic physics. This thesis aims to systematically study basic optical and magneto-optical proper- ties of several members of these materials, namely the lead tin salts crystalline topological insulators and graphene. Non-destructive experimental techniques of spectroscoipic ellipsometry and FTIR magneto-optical spectroscopy were used to perform measurements from IR to UV spectral regions. A four-band Hamilto- nian model was derived to describe the most important band structure features of PbSnSe system placed in a magnetic field. This allowed to extract the band structure and topological parameters of the investigated samples. Extensive mod- eling results based on...
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Variational Neural Network Quantum States for Frustrated Magnetic Systems
Mezera, Matěj ; Žonda, Martin (advisor) ; Slobodeniuk, Artur (referee)
We investigate the Shastry-Sutherland model (SSM), i.e., spin-1/2 quantum Heisen- berg model on a Shastry-Sutherland lattice, using a newly emerging approach exploiting well-developed machine learning techniques. We utilize neural networks as variational quantum states in quantum Monte Carlo investigations of the ground state properties. We first focus on SSM without an external magnetic field. For small lattices accessible via exact diagonalization, we compare the precision of various architectures based on re- stricted Boltzmann machines (RBM) or group-convolutional neural networks. The most versatile and precise architecture, namely complex-valued RBM, is then applied for larger lattices. Here we investigate the frustrated regime. We show that the RBM is able to represent all three of the major and fundamentally different phases of SSM. Finally, we apply the complex-valued RBM for SSM in a finite external magnetic field. We find that it cannot capture the intriguing steps in magnetization typical for SSM correctly due to its tendency to prefer more ordered states with higher magnetization. 1
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