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arXiv 0811.4375cond-mat.mtrl-sci
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非格点自学习动力学Monte Carlo方法在fcc(111)表面二维团簇扩散中的应用

Off-Lattice Self-Learning Kinetic Monte Carlo: Application to 2D Cluster Diffusion on the fcc(111) Surface

Oleg Trushin, Handan Yildirim, Abdelkader Kara, Talat S. Rahman

摘要:

我们报告的动态蒙特卡罗(KMC)方法的发展,提高了精度和增加的多功能性,用于描述金属表面上的原子扩散率。我们最近提出的自学习KMC(SLKMC)[1]中内置的晶格约束被释放,使原子可以自由占据晶格外位置,以适应负责小簇扩散,外围原子运动和异质外延生长的几个过程。该技术结合了嵌入在SLKMC方法与一个新的模式识别方案,适合于一个离格模型,其中相对原子位置被用来表征和存储配置的想法。应用相结合的阻力和斥力偏置势(RBP)的鞍点搜索方法,允许处理协同簇,多个和单原子运动的平等地位。这种串联的方法有助于揭示几个新的原子机制,有助于集群迁移。我们提出的应用程序,这种离格SLKMC的二维岛的铜(包含2至30个原子)在Cu和Ag(111),使用嵌入原子方法的原子间势。对于异质体系Cu/Ag(111),该技术揭示了涉及协调运动的机制,如剪切、呼吸和无公度占位。虽然该技术在存储和检索中引入了复杂性,但它不会引入明显的额外计算成本。

英文摘要:

We report developments of the kinetic Monte Carlo (KMC) method with improved accuracy and increased versatility for the description of atomic diffusivity on metal surfaces. The on-lattice constraint built into our recently proposed Self-Learning KMC (SLKMC) [1] is released, leaving atoms free to occupy Off-Lattice positions to accommodate several processes responsible for small-cluster diffusion, periphery atom motion and hetero-epitaxial growth. The technique combines the ideas embedded in the SLKMC method with a new pattern recognition scheme fitted to an Off-Lattice model in which relative atomic positions is used to characterize and store configurations. Application of a combination of the drag and the Repulsive Bias Potential (RBP) methods for saddle points searches, allows the treatment of concerted cluster, and multiple and single atom motions on equal footing. This tandem approach has helped reveal several new atomic mechanisms which contribute to cluster migration. We present applications of this Off-Lattice SLKMC to the diffusion of 2D islands of Cu (containing 2 to 30 atoms) on Cu and Ag(111), using interatomic potential from the Embedded Atom Method. For the hetero system Cu/Ag(111), this technique has uncovered mechanisms involving concerted motions such as shear, breathing and commensurate-incommensurate occupancies. Although the technique introduces complexities in storage and retrieval, it does not introduce noticeable extra computational cost.

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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