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arXiv 2406.04775cond-mat.mtrl-scicond-mat.dis-nncond-mat.mes-hallcond-mat.softcond-mat.stat-mech
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缺陷诱导石墨烯中的涟漪从动态到静态的相变

Defects induce phase transition from dynamic to static rippling in graphene

Fabian L. Thiemann, Camille Scalliet, Erich A. Müller, Angelos Michaelides

AI总结:

该研究利用机器学习驱动的分子动力学模拟,发现石墨烯中缺陷浓度超过临界值时会发生从动态涟漪到静态涟漪的相变,揭示了缺陷间弹性相互作用驱动该相变的统一原理,为设计具有定制涟漪动力学的二维器件及新型无序基催化与界面材料奠定基础。

摘要:

二维(2D)材料展现出纳米尺度的动态涟漪,这些涟漪显著影响其性质。晶格中的缺陷是调控材料形貌的基本构建单元。尽管已有一些研究探索了二维材料中缺陷结构与涟漪动力学之间的联系,但对这一关系的全面理解尚未实现。在此,我们利用机器学习驱动的分子动力学模拟来解决这一问题。具体而言,我们发现当缺陷浓度超过临界值时,自由悬浮的石墨烯片会发生从自由传播涟漪到静态涟漪的动态相变。我们的计算方法以原子分辨率捕捉了动力学过程,并揭示该相变由缺陷之间的弹性相互作用驱动。研究发现这些相互作用的强度因缺陷类型而异,我们识别出一组统一的原理,驱动二维材料中从动态到静态的相变。我们的工作不仅合理解释了有缺陷二维材料中令人困惑的实验结果,还为设计具有定制涟漪动力学的二维器件铺平了道路。这些见解可能为一类新型的基于无序的催化材料和界面材料奠定基础。

英文摘要:

Two-dimensional (2D) materials display nanoscale dynamic ripples that significantly impact their properties. Defects within the crystal lattice are the elementary building blocks to tailor the material's morphology. While some studies have explored the link between defective structures and rippling dynamics in 2D materials, a comprehensive understanding of this relationship has yet to be achieved. Here, we address this using machine learning-driven molecular dynamics simulations. Specifically, we find that above a critical concentration of defects, free-standing graphene sheets undergo a dynamic transition from freely propagating to static ripples. Our computational approach captures the dynamics with atomic resolution, and reveals that the transition is driven by elastic interactions between defects. The strength of these interactions is found to vary across defect types and we identify a unifying set of principles driving the dynamic-to-static transition in 2D materials. Our work not only rationalises puzzling experimental results for defective 2D materials, but also paves the way to design two-dimensional devices with tailored rippling dynamics. These insights could lay the foundations for a new class of disorder-based catalytic and interfacial materials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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