Topological transitions to Weyl states in bulk Bi2_2Se3_3: Effect of hydrostatic pressure and doping

Bi$_2$Se$_3$, a layered three dimensional (3D) material, exhibits topological insulating properties due to presence of surface states and a band gap of 0.3 eV in the bulk. We study the effect hydrostatic pressure $P$ and doping with rare earth elements on the topological aspect of this material in bulk from a first principles perspective. Our study shows that under a moderate pressure of P$>$7.9 GPa, the bulk electronic properties show a transition from an insulating to a Weyl semi-metal state due to band inversion. This electronic topological transition may be correlated to a structural change from a layered van der Waals material to a 3D system observed at $P$=7.9 GPa. At large $P$ density of states have significant value at the Fermi-energy. Intercalating Gd with a small doping fraction between Bi$_2$Se$_3$ layers drives the system to a metallic anti-ferromagnetic state, with Weyl nodes below the Fermi-energy. At the Weyl nodes time reversal symmetry is broken due to finite local field induced by large magnetic moments on Gd atoms. However, substituting Bi with Gd induces anti-ferromagnetic order with an increased direct band gap. Our study provides novel approaches to tune topological transitions, particularly in capturing the elusive Weyl semimetal states, in 3D topological materials

Topological transitions to Weyl states in bulk Bi2Se3:Effect of hydrostatic pressure and doping

Bi2Se3, a layered three-dimensional (3D) material, exhibits topological insulating properties due to the presence of surface states and a bandgap of 0.3 eV in the bulk. We study the effect of hydrostatic pressure P and doping with rare earth elements on the topological aspect of this material in bulk from a first principles perspective. Our study shows that under a moderate pressure of P . 7:9 GPa, the bulkelectronic properties show a transition from an insulating to a Weyl semi-metal state due to band inversion. This electronic topological transition may be correlated to a structural change from a layered van der Waals material to a 3D system observed at P ¼ 7:9 GPa. At large P, the density of states have a significant value at the Fermi energy. Intercalating Gd with a small doping fraction between Bi2Se3 layers drives the system to a metallic anti-ferromagnetic state, with Weyl nodes below the Fermi energy. At the Weyl nodes, time reversal symmetry is broken due to the finite local field induced by large magnetic moments on Gd atoms. However, substituting Bi with Gd induces anti-ferromagnetic order with an increased direct bandgap. Our study provides novel approaches to tune topological transitions, particularly in capturing the elusive Weyl semimetal states, in 3D topological materials

Proximate Dirac spin liquid in the J1-J3 XXZ model for honeycomb cobaltates

The concerted effort to find materials that host the enigmatic quantum spin liquid state has shone the spotlight on a variety of honeycomb cobaltates. While initially proposed as candidate realizations of the elusive Kitaev spin liquid, ab initio calculations and neutron scattering experiments have converged on their low energy description being a J1-J3 XXZ spin model with weak compass anisotropies. Here, we combine exact diagonalization and density-matrix renormalization group calculations with parton mean field theory and Gutzwiller wavefunctions, to argue for the presence of a proximate Dirac SL phase in the phase diagram of this model. This Dirac SL is shown, within parton mean field theory, to capture the broad continuum seen in the temperature (T ) and magnetic field dependent THz spectroscopy of BaCo2(AsO4)2. Weak disorder or zig-zag order coexisting with the SL is found to support a metallic thermal conductivity $\kappa \propto T$ as reported in NaCo2TeO6.Comment: 9 pages, 6 figure

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans