论文标题
Si（111） - （7 \ times7）表面重建的原子机制由人工神经网络潜力揭示
Atomistic Mechanism Underlying the Si(111)-(7\times7) Surface Reconstruction Revealed by Artificial Neural-network Potential
论文作者
论文摘要
Si（111）表面的7 \ Times7重建可以说是迄今为止在自然界观察到的最迷人的表面重建。然而，在六十年前被发现后，基于其形成的原子机制尚不清楚。在实验上，先验后观察到，因此对其形成机制的分析只能与考古学进行类比。从理论上讲，密度功能理论（DFT）正确预测了Si（111） - （7 \ times7）基态，但不切实际地模拟其形成过程；尽管经验潜力未能作为基态产生。我们开发了DFT质量的人工神经网络潜力，我们进行了准确的大规模模拟，以揭示Si（111） - （7 \ Times7）表面的形成。我们揭示了可能触发大量非保存的原子重排的序列介导的原子流行速率限制过程，最引人注目的是集体空位扩散的关键过程，该过程介导了一系列选择性二聚体，角孔，堆叠式，堆叠断层和二聚体模式形成，以履行7 \ time 7 \ time7 Reponstruction。我们的发现不仅可以解决这种著名的表面重建的长期谜团，而且还说明了机器学习在研究复杂结构中的力量。
The 7\times7 reconstruction of the Si(111) surface represents arguably the most fascinating surface reconstruction so far observed in nature. Yet, the atomistic mechanism underpinning its formation remains unclear after it was discovered sixty years ago. Experimentally, it is observed post priori so that analysis of its formation mechanism can only be carried out in analogy with archaeology. Theoretically, density-functional-theory (DFT) correctly predicts the Si(111)-(7\times7) ground state but is impractical to simulate its formation process; while empirical potentials failed to produce it as the ground state. Developing an artificial neural-network potential of DFT quality, we carried out accurate large-scale simulations to unravel the formation of the Si(111)-(7\times7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive non-conserved atomic rearrangements, most remarkably, a critical process of collective vacancy diffusion that mediates a sequence of selective dimer, corner-hole, stacking fault and dimer-line pattern formation, to fulfill the 7\times7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but also illustrate the power of machine learning in studying complex structures.
