First-principles Study of High-Pressure Phase Stability and Superconductivity of Bi4I4

Bismuth iodide Bi4I4 exhibits intricate crystal structures and topological insulating states that are highly susceptible to influence by environments, making its physical properties highly tunable by external conditions. In this work, we study the evolution of structural and electronic properties of Bi4I4 at high pressure using an advanced structure search method in conjunction with first-principles calculations. Our results indicate that the most stable ambient-pressure monoclinic α−Bi4I4 phase in C2/m symmetry transforms to a trigonal P31c structure (ɛ−Bi4I4) at 8.4 GPa, then to a tetragonal P4/mmm structure (ζ−Bi4I4) above 16.6 GPa. In contrast to the semiconducting nature of ambient-pressure Bi4I4, the two high-pressure phases are metallic, in agreement with reported electrical measurements. The ɛ−Bi4I4 phase exhibits distinct ionic states of Iδ− and (Bi4I3)δ + (δ=0.4123 e), driven by a pressure-induced volume reduction. We show that both ɛ- and ζ−Bi4I4 are superconductors, and the emergence of pressure-induced superconductivity might be intimately linked to the underlying structural phase transitions

Smooth Flow in Diamond: Atomistic Ductility and Electronic Conductivity

Diamond is the quintessential superhard material widely known for its stiff and brittle nature and large electronic band gap. In stark contrast to these established benchmarks, our first-principles studies unveil surprising intrinsic structural ductility and electronic conductivity in diamond under coexisting large shear and compressive strains. These complex loading conditions impede brittle fracture modes and promote atomistic ductility, triggering rare smooth plastic flow in the normally rigid diamond crystal. This extraordinary structural change induces a concomitant band gap closure, enabling smooth charge flow in deformation created conducting channels. These startling soft-and-conducting modes reveal unprecedented fundamental characteristics of diamond, with profound implications for elucidating and predicting diamond’s anomalous behaviors at extreme conditions

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs and distributed systems. Up to this point, no comparable effort has been made to parallelize the Elastic Net, despite its popularity in many high impact applications, including genetics, neuroscience and systems biology. The first contribution in this paper is of theoretical nature. We establish a tight link between two seemingly different algorithms and prove that Elastic Net regression can be reduced to SVM with squared hinge loss classification. Our second contribution is to derive a practical algorithm based on this reduction. The reduction enables us to utilize prior efforts in speeding up and parallelizing SVMs to obtain a highly optimized and parallel solver for the Elastic Net and Lasso. With a simple wrapper, consisting of only 11 lines of MATLAB code, we obtain an Elastic Net implementation that naturally utilizes GPU and multi-core CPUs. We demonstrate on twelve real world data sets, that our algorithm yields identical results as the popular (and highly optimized) glmnet implementation but is one or several orders of magnitude faster.Comment: 10 page