Exploring the defect landscape and dopability of chalcogenide perovskite BaZrS3

TL;DR

This study uses first principles calculations to analyze the defect landscape and dopability of BaZrS3, revealing its intrinsic n-type conductivity and challenges in achieving p-type doping, with implications for optoelectronic applications.

Contribution

It provides a comprehensive defect and dopant analysis of BaZrS3, including intrinsic and extrinsic defects, and offers a dataset for future machine learning defect predictions.

Findings

BaZrS3 is intrinsically n-type under various conditions.

La and Nb dopants enhance n-type conductivity.

Creating p-type BaZrS3 is difficult due to low formation energies of donor defects.

Abstract

BaZrS3 is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities that have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS3. In this work, we applied first principles computations to comprehensively investigate the defect landscape of BaZrS3, including all intrinsic defects and a set of selected impurities and dopants. BaZrS3 intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects, which made BaZrS3 even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS3 owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS3 paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys.