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<p>This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon self-interstitials and nitrogen-vacancy (NV) centres in diamond, focusing on their excited states and diffusion behaviour. From the simulated excited states, 'Bright', 'Spike', and 'Dark' defect configurations are classified based on their absorption spectrum features. Furthermore, machine learning molecular dynamics simulation provides insight into the possible diffusion mechanism of C<sub><i>i</i></sub> and NV, showing that C<sub><i>i</i></sub> can diffuse away or recombine with NV. The study yields new insight into the formation of NV defects in diamond for quantum technology applications.</p>

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Simulating the dynamics of NV− formation in diamond in the presence of carbon self-interstitials

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

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  • Guangzhao Chen,
  • Joseph C. A. Prentice,
  • Jason M. Smith

摘要

This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon self-interstitials and nitrogen-vacancy (NV) centres in diamond, focusing on their excited states and diffusion behaviour. From the simulated excited states, 'Bright', 'Spike', and 'Dark' defect configurations are classified based on their absorption spectrum features. Furthermore, machine learning molecular dynamics simulation provides insight into the possible diffusion mechanism of Ci and NV, showing that Ci can diffuse away or recombine with NV. The study yields new insight into the formation of NV defects in diamond for quantum technology applications.