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Surface phenomena are increasingly becoming important in exploring nanoscalematerials growth and characterization. Consequently, the need for atomisticbased simulations is increasing. Nevertheless, relying entirely on quantummechanical methods limits the length and time scales one can consider,resulting in an ever increasing dependence on alternative machine learningbased force fields. Recently, we proposed a machine learning approach, known asAGNI, that allows fast and accurate atomic force predictions given the atom'sneighborhood environment. Here, we make use of such force fields to study andcharacterize the nanoscale diffusion and growth processes occurring on an Al(111) surface. In particular we focus on the adatom ripening phenomena,confirming past experimental findings, wherein a low and high temperaturegrowth regime were observed, using entirely molecular dynamics simulations.

A study of adatom ripening on an Al (111) surface with machine learning force fields