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

Surface phenomena are increasingly becoming important in exploring nanoscale materials growth and characterization. Consequently, the need for atomistic based simulations is increasing. Nevertheless, relying entirely on quantum mechanical methods limits the length and time scales one can consider, resulting in an ever increasing dependence on alternative machine learning based force fields. Recently, we proposed a machine learning approach, known as AGNI, that allows fast and accurate atomic force predictions given the atom's neighborhood environment. Here, we make use of such force fields to study and characterize 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 temperature growth regime were observed, using entirely molecular dynamics simulations.

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