The Self Learning Kinetic Monte Carlo (SLKMC) method augmented with data analytics for adatom-island diffusion on surfaces

The Self-Learning Kinetic Monte Carlo (SLKMC) method with its usage of a pattern recognition enabled the collection of a large database of diffusion pathways and their energetics for two-dimensional adatom islands containing 2-50 atoms on fcc(111) metal surfaces [1]. A variety of diffusion mechanisms involving single and multiple island atoms were uncovered in long time (comparable to experiments) KMC simulations. In this talk, I will present results for the diffusion kinetics of two dimensional adatoms islands in homoepitaxial and heteroepitaxial systems. With examples of diffusion of Ag and Pd adatom islands on Ag(111) and Pd(111), and that of Cu and Ni adatoms islands on Ni(111) and Cu(111) [2], I will draw attention to the relative role of lateral interactions and binding energy in the size dependence of island diffusion characteristics. These and a few other descriptors have been the key for application of data driven techniques for the training of predictive models, which we find to yield activation energy barriers that are accurate and obtained with little computational cost. Efforts are also underway to obtain reliable neural network derived interatomic potentials that have accuracy similar to those obtained from density functional theory based calculations. These results are promising for the development of tools for multiscale modeling of morphological evolution of nanostructured systems.

1. G. Nandipati, A. Kara, S. I. Shah, and T. S. Rahman, Phys. Rev. B 88, 115402 (2013).
2. S. R. Acharya, S. I. Shah, and T. S. Rahman, Surface Science, 662, 42 (2017).