A few years back a Self-Learning Kinetic Monte Carlo (SLKMC) method [1] was introduced to examine temporal and spatial evolution of two-dimensional adatom islands containing 2-50 atoms on fcc(111) surfaces, unbiased by the choice of any apriori diffusion processes. The usage of pattern recognition and diffusion path finding schemes, together with reliable interaction potentials (semi-empirical and ab initio) enabled the collection of a large database for each island size, for a specific system, whose saturation signified its completeness. A variety of diffusion mechanisms involving single and multiple island atoms and concerted motion of the island as a whole were uncovered in long time (comparable to those in experimens) KMC simulations. Kinetically driven island shape changes and characteristics of island coarsening were also revealed in these large length and time scale simulations [2]. In this talk, after a review of present status of the technique, its successes and limitations, I will present results for the diffusion kinetics of two dimensional adatoms islands in two types of systems: homoepitaxial and heteroepitaxial. Drawing from the example of the diffusion of Ag and Pd adatom islands Ag(111) and Pd(111), and that of Cu and Ni adatoms islands on Ni(111) and Cu(111) [3], I will draw attention to the role of lateral interactions as revealed from calculations of energetic based on density functional theory. The thousands of diffusion processes and their energetics saved in the database provide fertile grounds for application of data driven techniques for extraction of descriptors and training of predictive models. I will present a summary of our recent success in extracting activation energy barriers applying techniques based on linear and non-linear statistical approaches that provide ultra-fast, yet accurate, barrier prediction which otherwise require intensive computational resources. These results are very promising for the development of tools suitable for multiscale modeling of the morphological evolution of nanostructured systems.

Self Learning Kinetic Monte Carlo (SLKMC) method for cluster diffusion on surfaces

2019 Spring National Meeting

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