Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond
Abstract
Diamond is an important host material for functional defects such as solid-state spin qubits. During the defect creation by irradiation and subsequent thermal annealing, carbon-related defects, including vacancies and interstitials, are generated and can migrate to form new defect complexes. To investigate this kinetically driven process and reveal the formation mechanisms of the di-interstitials and di-vacancies in the neutral charge state, we fine-tuned a universal machine learning interatomic potential using a small dataset generated from density functional theory calculations. We obtained a barrier of 1.84 eV for mono-interstitial migration and found that two initially separated mono-interstitials only need to overcome a lower barrier to form di-interstitial configurations. Beyond the training data, the MLIP also identified the formation pathway for an additional di-interstitial configuration and a stepwise divacancy dissociation mechanism. These results reveal migration-assisted defect complex formation mechanisms and demonstrate how targeted MLIP training can identify reaction pathways.
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BibTeX
@misc{huang2026machine,
title = {{Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond}},
author = {Grace Huang and Christopher Fivecoat and Xun Xu and Mengen Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26301},
eprint = {2610.26301},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26301},
}
RIS
TY - UNPB
TI - Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond
AU - Grace Huang
AU - Christopher Fivecoat
AU - Xun Xu
AU - Mengen Wang
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26301
UR - https://arxiv.org/abs/2610.26301
AB - Diamond is an important host material for functional defects such as solid-state spin qubits. During the defect creation by irradiation and subsequent thermal annealing, carbon-related defects, including vacancies and interstitials, are generated and can migrate to form new defect complexes. To investigate this kinetically driven process and reveal the formation mechanisms of the di-interstitials and di-vacancies in the neutral charge state, we fine-tuned a universal machine learning interatomic potential using a small dataset generated from density functional theory calculations. We obtained a barrier of 1.84 eV for mono-interstitial migration and found that two initially separated mono-interstitials only need to overcome a lower barrier to form di-interstitial configurations. Beyond the training data, the MLIP also identified the formation pathway for an additional di-interstitial configuration and a stepwise divacancy dissociation mechanism. These results reveal migration-assisted defect complex formation mechanisms and demonstrate how targeted MLIP training can identify reaction pathways.
ER -