A study of adatom ripening on an Al (1 1 1) surface with machine learning force fields

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• 作者：V. Botu ; ^{botuvenkatesh@gmail.com} ; J. Chapman ; R. Ramprasad
• 关键词：Molecular dynamics ; Machine learning ; Force field ; Island ripening ; Surface growth
• 刊名：Computational Materials Science
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：129
• 期：Complete
• 页码：332-335
• 全文大小：1109 K
• 卷排序：129

文摘

Surface phenomena are increasingly becoming important in exploring nanoscale materials growth and characterization. Consequently, the need for atomistic based simulations is increasing. Recently, we proposed a machine learning approach, known as AGNI, that allows fast and quantum mechanical accurate atomic force predictions given an 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 (1 1 1) 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.