Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond
Abstract
Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.
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BibTeX
@misc{chang2026machinelearningdriven,
title = {{Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond}},
author = {Xiaoya Chang and Nima Ghafari Cherati and Arsalan Hashemi and Mikko Karttunen and Ádám Gali and Tapio Ala-Nissila},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.19600},
eprint = {2606.19600},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.19600},
}
RIS
TY - UNPB
TI - Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond
AU - Xiaoya Chang
AU - Nima Ghafari Cherati
AU - Arsalan Hashemi
AU - Mikko Karttunen
AU - Ádám Gali
AU - Tapio Ala-Nissila
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2606.19600
UR - https://arxiv.org/abs/2606.19600
AB - Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.
ER -