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 [1] 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. A variety of diffusion mechanisms involving single and multiple island atoms were uncovered in long time (comparable to those in experiments) KMC simulations. In this talk, I will present results for the diffusion kinetics of two dimensional adatoms islands in two types of systems: homoepitaxial and heteroepitaxial. With examples 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 relative role of lateral interactions and binding energy in the size dependence of the island diffusion characteristics. Turning to the 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 that are accurate and obtained with little computational cost. These results are very promising for the development of tools suitable for multiscale modeling of the morphological evolution of nanostructured systems. Efforts in developing neural network derived interatomic potential informed from high throughput density functional theory calculations will also be presented.