AI 中文总结
研究MoS₂中硫空位团簇集体行为，用基于机器学习原子间势分子动力学模拟跃迁速率的动力学蒙特卡洛模拟，识别出三种输运状态，揭示空位扩散系数与平均缺陷浓度的强依赖关系，为MoS₂忆阻行为起源提供新见解。
AI 中文摘要
硫空位迁移对基于二硫化钼（MoS₂）的忆阻器和忆晶体管等器件的电子输运和功能行为有至关重要的影响。近期原子模拟表明，空位迁移通过协同的、空位辅助的硫跳跃进行，这意味着缺陷动力学存在强相关性。本文利用基于机器学习原子间势分子动力学模拟得出的跃迁速率，通过动力学蒙特卡洛模拟研究了MoS₂中硫空位团簇的集体行为。识别出三种输运状态：低浓度时，空位不动或局限于小团簇；高浓度时，观察到具有恒定扩散系数的经典扩散输运，空位聚集成各向异性扩展团簇；中间状态则是团簇合并成相互连接、波动的网络，扩散系数依赖浓度，团簇尺寸分布广泛。空位扩散系数对平均缺陷浓度的强烈依赖为MoS₂中忆阻行为的起源提供了新见解。
英文摘要
Sulfur vacancy migration has a crucial impact on electronic transport and the functional behavior of MoS$_2$-based devices such as memristors and memtransistors. According to recent atomistic simulations, vacancy migration proceeds via cooperative, vacancy-assisted sulfur jumps, implying strongly correlated defect dynamics. Here, we investigate the collective behavior of sulfur-vacancy clusters in MoS$_2$ using kinetic Monte-Carlo simulations with transition rates derived from machine learning interatomic potential molecular dynamics simulations. We identify three transport regimes: At low concentrations, vacancies are immobile or confined within small clusters, whereas at high concentrations, classical diffusive transport with a constant diffusion coefficient is observed, and vacancies aggregate into anisotropically extended clusters. A well defined intermediate regime is characterized by clusters merging into a connected, fluctuating network with a concentration-dependent diffusion coefficient. This regime is characterized by a broad distribution of cluster sizes. The strong dependence of the vacancy diffusion coefficient on the average defect concentration provides new insights into the origin of memristive behavior observed in MoS$_2$.
