  We present models for prediction of activation energy barrier of diffusion
process of adatom (1-4) islands obtained by using data-driven techniques. A set
of easily accessible features, geometric and energetic, that are extracted by
analyzing the variation of the energy barriers of a large number of processes
on homo-epitaxial metallic systems of Cu, Ni, Pd, and Ag are used along with
the activation energy barriers to train and test linear and non-linear
statistical models. A multivariate linear regression model trained with energy
barriers for Cu, Pd, and Ag systems explains 92% of the variation of energy
barriers of the Ni system, whereas the non-linear model using artificial neural
network slightly enhances the success to 93%. Next mode of calculation that
uses barriers of all four systems in training, predicts barriers of randomly
picked processes of those systems with significantly high correlation
coefficient: 94.4% in linear regression model and 97.7% in artificial neural
network model. Calculated kinetics parameters such as the type of frequently
executed processes and effective energy barrier for Ni dimer and trimer
diffusion on the Ni(111) surface obtained from KMC simulation using the
predicted (data-enabled) energy barriers are in close agreement with those
obtained by using energy barriers calculated from interatomic interaction
potential.

Materials Science;Disordered Systems and Neural Networks;Atomic and Molecular Clusters;Data Analysis, Statistics and Probability