Topological Semimetal-Insulator Quantum Phase Transition in Zintl Compounds Ba2X (X=Si, Ge)

By first-principles calculations, we find that Ba2X(X=Si, Ge) hosts a topological semimetal phase with one nodal ring in the kx=0 plane, which is protected by the glide mirror symmetry when spin-orbit coupling (SOC) is ignored. The corresponding drumheadlike surface flat band appears on the (100) surface in surface Green function calculation. Furthermore, a topological-semimetal-to-insulator transition (TSMIT) is found. The nodal line semimetal would evolve into topological insulator as SOC is turned on. The topologically protected metallic surface states emerge around the Gamma=0 point, which could be tuned into topologically-trivial insulator state by more than 3% hydrostatic strain. These results reveal a new category of materials showing quantum phase transition between topological semimetal and insulator, and tunability through elastic strain engineering.Comment: 14 pages. 4 figure

Efficient Latent Semantic Extraction from Cross Domain Data with Declarative Language

With large amounts of data continuously generated by intelligence devices, efficient analysis of huge data collections to unearth valuable insights has become one of the most elusive challenges for both academia and industry. The key elements to establishing a scalable analyzing framework should involve (1) an intuitive interface to describe the desired outcome, (2) a well-crafted model that integrates all available information sources to derive the optimal outcome and (3) an efficient algorithm that performs the data integration and extraction within a reasonable amount of time. In this dissertation, we address these challenges by proposing (1) a cross-language interface for a succinct expression of recursive queries, (2) a domain specific neural network model that can incorporate information of multiple modalities, and (3) a sample efficient training method that can be used even for extremely-large output-class classifiers. Our contributions in this thesis are thus threefold: First, for the ubiquitous recursive queries in advanced data analytics, on top of BigDatalog and Apache Spark, we design a succinct and expressive analytics tool encapsulating the functionality and classical algorithms of Datalog, a quintessential logic programming language. We provide the Logical Library (LLib), a Spark MLlib-like high-level API supporting a wide range of recursive algorithms and the Logical DataFrame (LFrame), an extension to Spark DataFrame supporting both relational and logical operations. The LLib and LFrame enable smooth collaborations between logical applications and other Spark libraries and cross-language logical programming in Scala, Java, or Python. Second, we utilize variants of recurrent neural network (RNN) to incorporate some enlightening sequential information overlooked by the conventional works in two different domains including Spoken Language Understanding (SLU) and Internet Embedding (IE). In SLU, we address the problem caused by solely relying on the first best interpretation (hypothesis) of an audio command through a series of new architectures comprising bidirectional LSTM and pooling layers to jointly utilize the other hypotheses' texts or embedding vectors, which are neglected but with valuable information missed by the first best hypothesis. In IE, we propose the DIP, an extension of RNN, to build up the internet coordinate system with the IP address sequences, which are also unnoticed in conventional distance-based internet embedding algorithms but encode structural information of the network. Both DIP and the integration of all hypotheses bring significant performance improvements for the corresponding downstream tasks. Finally, we investigate the training algorithm for multi-class classifiers with a large output-class size, which is common in deep neural networks and typically implemented as a softmax final layer with one output neuron per each class. To avoid expensive computing the intractable normalizing constant of softmax for each training data point, we analyze the well-known negative sampling and improve it to the amplified negative sampling algorithm, which gains much higher performance with lower training cost

Thermal Transport for Probing Quantum Materials

Thermal transport is less appreciated in probing quantum materials in comparison to electrical transport. This article aims to show the pivotal role that thermal transport may play in understanding quantum materials: the longitudinal thermal transport reflects the itinerant quasiparticles even in an electrical insulating phase, while the transverse thermal transport such as thermal Hall and Nernst effect are tightly linked to nontrivial topology. We discuss three types of examples: quantum spin liquids where thermal transport identifies its existence, superconductors where thermal transport reveals the superconducting gap structure, and topological Weyl semimetals where anomalous Nernst effect is a consequence of nontrivial Berry curvature. We conclude with an outlook of the unique insights thermal transport may offer to probe a much broader category of quantum phenomena.Comment: A short review article with 6 figures. Comments are welcome

Single Particle Transport in Two-dimensional Heterojunction Interlayer Tunneling Field Effect Transistor

The single particle tunneling in a vertical stack consisting of monolayers of two-dimensional semiconductors is studied theoretically and its application to a novel Two-dimensional Heterojunction Interlayer Tunneling Field Effect Transistor (Thin-TFET) is proposed and described. The tunneling current is calculated by using a formalism based on the Bardeen's transfer Hamiltonian, and including a semi-classical treatment of scattering and energy broadening effects. The misalignment between the two 2D materials is also studied and found to influence the magnitude of the tunneling current, but have a modest impact on its gate voltage dependence. Our simulation results suggest that the Thin-TFETs can achieve very steep subthreshold swing, whose lower limit is ultimately set by the band tails in the energy gaps of the 2D materials produced by energy broadening. The Thin-TFET is thus very promising as a low voltage, low energy solid state electronic switch