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Register a new userA Computational Framework for Automation of Point Defect Calculations
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A complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory has been developed. The framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. The package provides the capability to compute widely-accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In$_2$O$_3$ as test examples, we demonstrate the package capabilities and validate the methodology.
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First-principles calculations of charged defects have become a cornerstone of research in semiconductors and insulators by providing insights into their fundamental physical properties. But current standard approach using the so-called jellium model has encountered both conceptual ambiguity and computational difficulty, especially for low-dimensional semiconducting materials. In this Communication, we propose a physical, straightforward, and dimension-independent universal model to calculate the formation energies of charged defects in both three-dimensional (3D) bulk and low-dimensional semiconductors. Within this model, the ionized electrons or holes are placed on the realistic host band-edge states instead of the virtual jellium state, therefore, rendering it not only naturally keeps the supercell charge neutral, but also has clear physical meaning. This realistic model reproduces the same accuracy as the traditional jellium model for most of the 3D semiconducting materials, and remarkably, for the low-dimensional structures, it is able to cure the divergence caused by the artificial long-range electrostatic energy introduced in the jellium model, and hence gives meaningful formation energies of defects in charged state and transition energy levels of the corresponding defects. Our realistic method, therefore, will have significant impact for the study of defect physics in all low-dimensional systems including quantum dots, nanowires, surfaces, interfaces, and 2D materials.
The search for new materials, based on computational screening, relies on methods that accurately predict, in an automatic manner, total energy, atomic-scale geometries, and other fundamental characteristics of materials. Many technologically important material properties directly stem from the electronic structure of a material, but the usual workhorse for total energies, namely density-functional theory, is plagued by fundamental shortcomings and errors from approximate exchange-correlation functionals in its prediction of the electronic structure. At variance, the $GW$ method is currently the state-of-the-art {em ab initio} approach for accurate electronic structure. It is mostly used to perturbatively correct density-functional theory results, but is however computationally demanding and also requires expert knowledge to give accurate results. Accordingly, it is not presently used in high-throughput screening: fully automatized algorithms for setting up the calculations and determining convergence are lacking. In this work we develop such a method and, as a first application, use it to validate the accuracy of $G_0W_0$ using the PBE starting point, and the Godby-Needs plasmon pole model ($G_0W_0^textrm{GN}$@PBE), on a set of about 80 solids. The results of the automatic convergence study utilized provides valuable insights. Indeed, we find correlations between computational parameters that can be used to further improve the automatization of $GW$ calculations. Moreover, we find that $G_0W_0^textrm{GN}$@PBE shows a correlation between the PBE and the $G_0W_0^textrm{GN}$@PBE gaps that is much stronger than that between $GW$ and experimental gaps. However, the $G_0W_0^textrm{GN}$@PBE gaps still describe the experimental gaps more accurately than a linear model based on the PBE gaps.
Deep level transient spectroscopy (DLTS) is used extensively to study defects in semiconductors. We demonstrate that great care should be exercised in interpreting activation energies extracted from DLTS as ionization energies. We show how first-principles calculations of thermodynamic transition levels, temperature effects of ionization energies, and nonradiative capture coefficients can be used to accurately determine actual activation energies that can be directly compared with DLTS. Our analysis is illustrated with hybrid functional calculations for two important defects in GaN that have similar thermodynamic transition levels, and shows that the activation energy extracted from DLTS includes a capture barrier that is temperature dependent, unique to each defect, and in some cases large in comparison to the ionization energy. By calculating quantities that can be directly compared with experiment, first-principles calculations thus offer powerful leverage in identifying the microscopic origin of defects detected in DLTS.
In this work we have studied the defect structure and corresponding defect concentration investigations through the theoretical, experimental and computational works on B2-type Fe-Al alloys. We have used ab initio framework in order to investigate the defect structure. To have a proper explanation for high defect concentration in B2-FeAl, we did not confine with point defect, but extend the work on defect complexes. The possible defect formation energies were calculated with the dependence of chemical potential and carefully investigated against supercell size and the effect of magnetism. The calculations revealed that the double Fe antisite at Fe rich condition, the single Fe vacancy at intermediate region (i.e in the stoichiometry) and the double Al antisite is the dominant defect close to Al rich condition, where mainly Al rich region was unstable. From the obtained defect formation energies, defect concentrations were calculated at different temperatures with respect to Al concentration for B2-FeAl. It has been found that increasing Al content and temperature gradually leads to increase in the vacancy content. It has also seen that the dominant defect for all temperature ranges was the single Fe vacancy at the exact stoichiometry and the highest single Fe vacancy content detected with 1.6 % at 1450 K.
Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework that models the speakers word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.
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