Prediction of activation energy barrier of island diffusion processes using data-driven approaches

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.