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

TL;DR

This paper combines advanced computational methods to explore how carbon self-interstitials interact with nitrogen-vacancy centers in diamond, revealing their excited states and diffusion mechanisms relevant for quantum technology.

Contribution

It introduces a new machine-learning potential for carbon and nitrogen and applies it to simulate NV^- formation dynamics in diamond.

Findings

Identification of three defect configurations: Bright, Spike, and Dark.

Insights into diffusion pathways of carbon interstitials and NV centers.

Potential implications for optimizing diamond-based quantum devices.

Abstract

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) centers 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.