Spinney: post-processing of first-principles calculations of point defects in semiconductors with Python

TL;DR

Spinney is an open-source Python tool that streamlines post-processing of first-principles defect calculations in semiconductors, enabling efficient analysis of defect properties and stability for materials design.

Contribution

The paper introduces Spinney, a novel Python package that automates and enhances post-processing of DFT defect calculations, including electrostatic corrections and defect level analysis.

Findings

Successfully applied to c-BN, Mg-doped GaN, TiO2, and ZnO

Automates calculation of defect formation energies and charge transition levels

Improves accuracy and efficiency of defect property analysis

Abstract

Understanding and predicting the thermodynamic properties of point defects in semiconductors and insulators would greatly aid in the design of novel materials and allow tuning the properties of existing ones. As a matter of fact, first-principles calculations based on density functional theory (DFT) and the supercell approach have become a standard tool for the study of point defects in solids. However, in the dilute limit, of most interest for the design of novel semiconductor materials, the raw DFT calculations require an extensive post-processing. Spinney is an open-source Python package developed with the aim of processing first-principles calculations to obtain several quantities of interest, such as the chemical potential limits that assure the thermodynamic stability of the defectladen system, defect charge transition levels, defect formation energies, including electrostatic corrections for finite-size effects, and defect and carrier concentrations. In this paper we demonstrate the capabilities of the Spinney code using c-BN, Mg-doped GaN, TiO2 and ZnO as examples.