Hexagonal BN (h-BN) is attracting a lot of attention for two-dimensional electronics and as a host for single-photon emitters. We study the properties of native defects and impurities in h-BN using density functional theory with a hybrid functional. Native vacancy and antisite defects have high formation energies, and are unlikely to form under thermodynamic equilibrium for typical growth conditions. Self-interstitials can have low formation energies when the Fermi level is near the band edges, and may form as charge compensating centers; however, their low migration barriers render them highly mobile, and they are unlikely to be present as isolated defects. The defect chemistry of h-BN is most likely dominated by defects involving carbon, oxygen, and hydrogen impurities. Substitutional carbon and oxygen, as well as interstitial hydrogen and boron vacancy-hydrogen complexes, are low-energy defects in h-BN. Based on our results, we can rule out several proposed sources for defect-related luminescence in h-BN. In particular, we find that the frequently observed 4.1 eV emission cannot be associated with recombination at CN, as has been commonly assumed. We suggest alternative assignments for the origins of this emission, with CB as a candidate. We also discuss possible defect origins for the recently observed single-photon emission in h-BN, identifying interstitials or their complexes as plausible centers.
Hexagonal boron nitride (h-BN) is widely used in twodimensional electronics and serves as a host for single-photon emitters. We study the electronic structure of h-BN as a function of the number of layers and take into account different stacking configurations. Using first-principles calculations based on a hybrid functional, we find that the band gap of a single monolayer is direct, while for thicknesses above a monolayer, the band gap is indirect. By examining the positions of the band edges with respect to the vacuum level, we find this direct-to-indirect transition to be driven by a shift in the conduction-band minimum at the M point; this shift changes the band gap by 0.5 eV, going from a single monolayer to bulk. We analyze these results in terms of the orbital composition of the band edges at different high-symmetry points in the Brillouin zone.
Cathodoluminescence spectra have been measured to determine the characteristics of ubiquitous green luminescence (GL) in nonstoichiometric zinc oxide (ZnO). Zn-and O-rich ZnO were found to exhibit characteristic emissions at 2.53 eV [full width at half-maximum (FWHM) 340 meV] and 2.30 eV (FWHM 450 meV), respectively. Hydrogen was used to probe the physical nature of GL centers. The Zn-rich GL is enhanced upon H incorporation, whereas the O-rich GL is completely quenched as its underlying acceptor-like V Zn centers are passivated by H. The GL emission bands each exhibit remarkably different excitation-power dependencies. The Zn-rich GL follows a close to linear relationship with excitation power, while the O-rich GL exhibits a square-root dependence. Calculations based on bimolecular recombination equations show the defect concentration in Zn-rich ZnO is three orders of magnitude greater than that in O-rich ZnO, indicating V O is more readily formed than V Zn in thermochemical treatments of ZnO.
Over the past decades, the number of published materials science articles has increased manyfold. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw-text of published articles onto a structured database entry that allows for programmatic querying. To this end, we apply text-mining with named entity recognition (NER), along with entity normalization, for large-scale information extraction from the published materials science literature. The NER is based on supervised machine learning with a recurrent neural network architecture, and the model is trained to extract summary-level information from materials science documents, including: inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifer, with an overall accuracy (f1) of 87% on a test set, is applied to information extraction from 3.27 million materials science abstracts-the most information-dense section of published articles.Overall, we extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. Our database shows far greater recall in document retrieval when compared to traditional text-based searches due to an entity normalization procedure that recognizes synonyms. We demonstrate that simple database queries can be used to answer complex \meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data has been made freely available for bulk download; we have also made a public facing application programming interface (https://github.com/materialsintelligence/matscholar) and website http://matscholar.herokuapp.com/search for easy interfacing with the data, trained models and functionality described in this paper. These results will allow researchers to access targeted information on a scale and with a speed that has not been previously available, and can be expected to accelerate the pace of future materials science discovery.
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