金刚石中碳间隙簇的机器学习预测
Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond
AI总结：
通过主动学习构建间隙数据集，并基准测试三种机器学习原子间势，发现MACE势能准确预测能量和稳定性，而分子动力学模拟揭示了新的碳间隙簇及其亚稳态机制。
摘要：
金刚石中承载着对量子技术至关重要的光学活性点缺陷，然而在生长和辐照过程中引入的碳自间隙原子会与它们竞争并形成新缺陷，其构型景观由于微妙的能量差异控制着竞争极小值和路径而鲜有研究。这里我们通过主动学习构建了一个以间隙为中心的数据集，并基准测试了三种机器学习原子间势——GAP、NEP和等变MACE——与密度泛函理论在能量、力和迁移势垒方面的表现。MACE再现了参考能量学和相对稳定性，而其他势可能错误排序基态。使用经过验证的势进行退火分子动力学，揭示了一系列先前未报道的碳间隙簇，从双间隙到八间隙——其中几个引入了作为色心感兴趣的带隙态——并表明它们的亚稳态由动力学可及路径而非能量排序控制。这些结果绘制了间隙缺陷景观，并加速了量子技术的缺陷发现。
英文摘要：
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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