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

TL;DR

PyCDT is a Python toolkit designed to streamline the setup and analysis of point defect calculations in semiconductors and insulators using DFT, facilitating high-throughput and reproducible research.

Contribution

The paper introduces PyCDT, a user-friendly Python toolkit that automates defect calculation workflows and integrates with the Materials Project database for efficient defect property analysis.

Findings

PyCDT simplifies defect calculation setup and post-processing.

It enables high-throughput, reproducible defect studies.

Demonstrated with GaAs semiconductor application.

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.