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

Abstract

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.

Assessing Local Structure Motifs Using Order Parameters for Motif Recognition, Interstitial Identification, and Diffusion Path Characterization

“…We now apply the validated order parameters based structure motif recognition criteria and the order parameters themselves (as a degree of perfect-motif resemblance) to automatically find structure motifs in a large materials database , determine interstitials (Broberg et al, 2016), and analyze the coordination environment along solid-state jump-diffusion paths (Rong et al, 2015).…”

“…We introduced an effective validation framework-the Einstein crystal test rig-which subjects all atoms in a (prototype) structure to well-defined (random) distortions, thus, systematically sounding out the robustness of any motif recognition approach. We then applied our approach successfully to three important applications in (computational) materials science: (i) mapping the structural character of a materials database via element-specific relative structure-motif occurrence plots, (ii) effective interstitial finding (InFiT tool developed here; cf., Broberg et al (2016)), and ion jump-diffusion path characterization (Rong et al, 2015). Our effective and efficient motif-recognition and assessment capabilities are freely available through the Python package pymatgen .…”

“…This is evidenced by a series (Decoster et al, 2008(Decoster et al, , 2009a(Decoster et al, ,b, 2010a(Decoster et al, ,b, 2012Pereira et al, 2011Pereira et al, , 2012Amorim et al, 2013;Silva et al, 2014) of β − emission channeling measurements (Hofsäss and Lindner, 1991;Silva et al, 2013) which were conducted at CERN's ISOLDE beamline. Those measurements inspired us to develop our Interstitialcy Finding Tool (InFiT), which is already used by the Python Charged Defect Tools (PyCDT)-a Python package for automatic setup and analysis of isolated charged defect calculations (Broberg et al, 2016).…”

“…For each point of the grid that is not closer than 1 Å to any crystal atom, the algorithm (Broberg et al, 2016) goes through following steps:…”

Properties of Co‐Evaporated RbInSe2Thin Films

“…The structures were considered to be optimized when the residual force on each atom was less than 0.05 eV Å −1 . Defect calculations were performed with the PyCDT toolkit, which creates input structures for the different charged defects, whereas the defect formation energies were calculated by taking finite‐size corrections into account.…”