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

TL;DR

This paper employs machine learning force fields to simulate and analyze adatom ripening on an Al (111) surface, revealing temperature-dependent growth regimes and confirming experimental observations through atomistic simulations.

Contribution

It introduces the application of the AGNI machine learning force field to study nanoscale surface phenomena, enabling accurate and efficient molecular dynamics simulations.

Findings

Confirmed low and high temperature growth regimes

Demonstrated the effectiveness of ML force fields in surface diffusion studies

Provided atomistic insights into adatom ripening processes

Abstract

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.