Large Rashba splittings in bulk and monolayer of BiAs

Two-dimensional materials with Rashba split bands near the Fermi level are key to developing upcoming next-generation spintronics. They enable generating, detecting, and manipulating spin currents without an external magnetic field. Here, we propose BiAs as a novel layered semiconductor with large Rashba splitting in bulk and monolayer forms. Using first-principles calculations, we determined the lowest energy structure of BiAs and its basic electronic properties. Bulk BiAs has a layered crystal structure with two atoms in a rhombohedral primitive cell, similar to the parent Bi and As elemental phases. It is a semiconductor with a narrow and indirect band gap. The spin-orbit coupling leads to Rashba-Dresselhaus spin splitting and characteristic spin texture around the L-point in the Brillouin zone of the hexagonal conventional unit cell, with Rashba energy and Rashba coupling constant for valence (conduction) band of $E_R$= 137 meV (93 meV) and $\alpha_R$= 6.05 eV\AA~(4.6 eV{\AA}). In monolayer form (i.e., composed of a BiAs bilayer), BiAs has a much larger and direct band gap at $\Gamma$, with a circular spin texture characteristic of a pure Rashba effect. The Rashba energy $E_R$= 18 meV and Rashba coupling constant $\alpha_R$= 1.67 eV{\AA} of monolayer BiAs are quite large compared to other known 2D materials, and these values are shown to increase under tensile biaxial strain.Comment: 15pages,9figure

The deep-acceptor nature of the chalcogen vacancies in 2D transition-metal dichalcogenides

Chalcogen vacancies in the semiconducting monolayer transition-metal dichalcogenides (TMDs) have frequently been invoked to explain a wide range of phenomena, including both unintentional p-type and n-type conductivity, as well as sub-band gap defect levels measured via tunneling or optical spectroscopy. These conflicting interpretations of the deep versus shallow nature of the chalcogen vacancies are due in part to shortcomings in prior first-principles calculations of defects in the semiconducting two-dimensional (2D) TMDs that have been used to explain experimental observations. Here we report results of hybrid density functional calculations for the chalcogen vacancy in a series of monolayer TMDs, correctly referencing the thermodynamic charge transition levels to the fundamental band gap (as opposed to the optical band gap). We find that the chalcogen vacancies are deep acceptors and cannot lead to n-type or p-type conductivity. Both the (0/$-1$) and ($-$1/$-$2) transition levels occur in the gap, leading to paramagnetic charge states S=1/2 and S=1, respectively, in a collinear-spin representation. We discuss trends in terms of the band alignments between the TMDs, which can serve as a guide to future experimental studies of vacancy behavior

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band gap correction of ~1.5 eV for this class of materials, where ~1 eV comes from downward shifting the valence band and ~0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure

In Silico Design of Three-Dimensional Porous Covalent Organic Frameworks via Known Synthesis Routes and Commercially Available Species

Covalent organic frameworks (COFs) are a class of advanced nanoporous polymeric materials which combine the crystallinity of metalorganic frameworks (MOFs) with the stability and potentially low-cost organic chemistry of porous polymer networks (PPNs). Like other advanced porous materials, COFs can potentially be designed to meet the needs of a variety of applications, from energy, to security, to human health. In this work, we construct in silico a database of hypothetical three-dimensional, crystalline COFs. In constructing this library we generate novel COFs using only established synthetic routes, previously utilized tetrahedral building units, and commercially available bridging linker molecules. This ensures that there are no known chemical barriers to synthesizing all materials in our database. We relaxed all materials in our database through semiempirical electronic structure calculations. In addition, for those structures that allow interpenetration, we designed interpenetrated versions of the basic structure. Then, we characterized the porosity of each of these structures. The final set of 4147 structures (based on 620 unique noninterpenetrated structures) and their computed properties are publicly available and can be screened to identify promising materials for a wide variety of applications. Here, we assess the suitability of our COFs for vehicular methane storage by performing molecular simulations to predict the equilibrium methane uptake

PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. We anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and that it will provide a foundation for automated, high-throughput calculations. Program summary: Program title: PyCDT Program Files doi: http://dx.doi.org/10.17632/7vzk5gxzh3.1 Licensing Provisions: MIT License. Programming language: Python External routines/libraries: NumPy [1], matplotlib [2], and Pymatgen [3], Nature of problem: Computing the formation energies and stable point defects with finite size supercell error corrections for charged defects in semiconductors and insulators. Solution method: Automated setup, and parsing of defect calculations, combined with local use of finite size supercell corrections. All combined into a code with a standard user-friendly command line interface that leverages a core set of tools with a wide range of applicability. Additional comments: This article describes version 1.0.0. Program obtainable from https://bitbucket.org/mbkumar/pycd

PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. We anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and that it will provide a foundation for automated, high-throughput calculations