Cluster Diffusion and Coalescence on Metal Surfaces: Applications of a Self-learning Kinetic Monte-Carlo method
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Cluster Diffusion and Coalescence on Metal Surfaces: applications of a Self-learning Kinetic Monte-Carlo method Talat S. Rahman1*, Abdelkader Kara1, Altaf Karim1, Oleg Trushin2 1 Department of Physics, Cardwell Hall, Kansas State University, Manhattan, KS 66506 2 Institute of Microelectronics and Informatics, Russian Academy of Sciences, Yaroslavl 150007, Russia. *email:[email protected] ABSTRACT
The Kinetic Monte Carlo (KMC) method has become an important tool for examination of phenomena like surface diffusion and thin film growth because of its ability to carry out simulations for time scales that are relevant to experiments. But the method generally has limited predictive power because of its reliance on predetermined atomic events and their energetics as input. We present a novel method, within the lattice gas model in which we combine standard KMC with automatic generation of a table of microscopic events, facilitated by a pattern recognition scheme. Each time the system encounters a new configuration, the algorithm initiates a procedure for saddle point search around a given energy minimum. Nontrivial paths are thus selected and the fully characterized transition path is permanently recorded in a database for future usage. The system thus automatically builds up all possible single and multiple atom processes that it needs for a sustained simulation. Application of the method to the examination of the diffusion of 2-dimensional adatom clusters on Cu(111) displays the key role played by specific diffusion processes and also reveals the presence of a number of multiple atom processes, whose importance is found to decrease with increasing cluster size and decreasing surface temperature. Similarly, the rate limiting steps in the coalescence of adatom islands are determined. Results are compared with those from experiments where available and with those from KMC simulations based on a fixed catalogue of diffusion processes.
I. INTRODUCTION The topic of this MRS symposium “Modeling of Morphological Evolution on Surfaces and Interfaces,” is timely and important because of its relevance to the development of an understanding of microscopic processes that control thin film growth and its temporal evolution. Such theoretical and computational studies nicely complement and supplement experimental observations in the area. Together this three-pronged approach is necessary if we are to build materials whose properties we can control and predict. This is not an easy task because studies of systems of realistic dimensions demand seamless integration of information obtained at the microscopic level into formulations which predict and characterize behavior of systems at the macroscopic scale. We are speaking here of differences of many orders of magnitude. Phenomena at the atomic level extend themselves over nanometers with characteristic time
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scales lying in the range of femto (10-15) to pico (10-12) seconds, while thin films for industrial applications are of mesoscopic (~microns) or macroscopic (>mi
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