School of Science Department of Physics 55 High-quality Two-dimensional Heterostructural Device: From Materials Synthesis to Device Fabrication Supervisor: LEI Shiming / PHYS Student: ZHAO, Heze / PHYS-IRE Course: UROP 1100, Spring UROP 2100, Summer Rare earth tellurides (RTe3, R2Te5, RTe2, etc., R = rare earth elements) in their free-standing 2-dimensional (2D) limit lacks relevant study in its electromagnetic properties, and could potentially be intercalated with the newly reported novel transport of electrodes. This intercalation is expected to bring novel phases such as the coexistence of superconductivity and magnetism. We synthesize RTe3 from bulk and explore its potentials of such intercalation by fabrication of heterostructural devices. From characterization of the bulk materials and comparing with published results, we have validated their quality. From the bulk materials, we have fabricated 2D heterostructures following the exfoliation-transferring workflow. After transferred onto electrodes, the devices are tested for their electromagnetic properties. Electromagnetic and Acoustic Metasurfaces Supervisor: LI Jensen Tsan Hang / PHYS Student: WONG Chi Wai / MECH Course: UROP 1000, Summer In this literature review, it is separated into several part which are metasurface, the split ring resonators and continuous wires, standard dielectric resonator and 3D dielectric composite, acoustic material experiment (soft rubber in water). Each part will explain from theoretical and simulation result to experimental result in detail. At last, a summary will be given and also include the current research on metamaterial. Self-learning Monte Carlo with Deep Neural Networks Supervisor: LIU Junwei / PHYS Student: YU Ho Hin / PHYS-IRE Course: UROP 1100, Summer A deep neural network can be used to approximate the Hamiltonian of a quantum many-body system, which performs significantly better at Monte Carlo simulations. This is commonly known as the self-learning monte carlo. This report discusses the theoretical background of the self-learning monte carlo. Firstly, second quantization is introduced to explain the notations frequently used in quantum many-body physics. Then, the Anderson impurity model will be introduced, as it will be used to test the performance of the self-learning monte carlo. Example matrices of the model are also provided. Finally, the Hubbard-Stratonovich transformation is discussed and applied on the Anderson impurity model to decouple the many-body operators.
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