Computationally Predicted Electronic Properties and Energetics of Native Defects in Cubic Boron Nitride
Ngoc Linh Nguyen, Hung The Dang, Tien Lam Pham, and Thi Minh Hoa, Nghiem

TL;DR
This paper uses first-principles calculations to analyze native defects in cubic boron nitride, linking defect types to electronic properties and photoluminescence spectra, with results aligning well with experiments.
Contribution
It provides a comprehensive computational study of native point defects in cubic boron nitride, including their electronic, structural, and optical properties, and correlates defect states with experimental photoluminescence data.
Findings
Defects like N_B antisite and B_i interstitial cause specific photoluminescent peaks.
Calculated defect formation energies and charge states match experimental stability data.
Defect-induced electronic states within the band gap are characterized.
Abstract
In this study, we employ a first-principles approach to conduct a comprehensive investigation of the properties of nine common native point defects in cubic boron nitride. This analysis combines standard semi-local and dielectric hybrid density-exchange-correlation functional calculations, encompassing vacancies, interstitials, antisites, and their complexes. Our findings elucidate the influence of these defects on the structural and electronic characteristics of cubic boron nitride, such as local structures, formation energy, magnetism, and the energies of defect states within the band gap. Notably, we accurately simulate the photoluminescent spectra of cubic boron nitride induced by these defects, demonstrating excellent agreement with experimental observations. This outcome indicates that the prominent peaks in the photoluminescent spectrum at 2.5 and 2.8 eV can be attributed to the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBoron and Carbon Nanomaterials Research · Semiconductor materials and devices · Machine Learning in Materials Science
