Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond

Overview & Synopsis

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.