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

被引：32

作者：

Botu, V. ^{[1
]}

Chapman, J. ^{[1
]}

Ramprasad, R. ^{[1
]}

机构：

[1] Univ Connecticut, Storrs, CT 06269 USA

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2017年 / 129卷

关键词：

Molecular dynamics; Machine learning; Force field; Island ripening; Surface growth; ATOMIC LAYER DEPOSITION; DENSITY-FUNCTIONAL THEORY; AUGMENTED-WAVE METHOD; MOLECULAR-DYNAMICS; SELF-DIFFUSION; GROWTH; AL(111); SIMULATIONS; ENERGIES; METALS;

D O I：

10.1016/j.commatsci.2016.12.007

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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 (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. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：332 / 335

页数：4

共 30 条

[21] Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces [J].