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首页> 外文期刊>Computer physics communications >PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators
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PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

机译：Pycdt：用于模拟半导体和绝缘子的点缺陷的Python工具包

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摘要

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.
机译：点缺陷对技术应用中使用的半导体和绝缘材料的性能产生了强烈影响，跨越微电子能量转换和储存。主导缺陷类型的性质，它们如何随加工条件而变化，以及它们对材料特性的影响是确定某种应用中材料性能的中心方面。然而，此信息难以直接从实验测量中访问。因此，基于电子密度泛函理论（DFT）的计算方法在点缺陷属性的计算中发现广泛使用。在这里，我们开发了Python收费的缺陷工具包（Pycdt），以加快使用广泛使用的DFT软件的缺陷计算的设置和后处理。 Pycdt具有用户友好的命令行界面，并提供与材料项目数据库的直接接口。这允许为任何感兴趣的材料以及后处理和应用最先进的静电校正项来设置许多带电缺陷计算。我们的论文作为Pycdt的文档，并证明其在应用于学习的GaAs化合物半导体的应用中。我们预计PyCDT代码将作为承担易于可重复的带电点缺陷属性计算的框架，并且它将为自动化的高通量计算提供基础。

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