Harry Handoko Halim^1*, Ryo Ueda^1*, Yoshitada Morikawa^1,2*

1 Department of Precision Engineering, Graduate School of Engineering, Osaka University, 2-1, Yamada-oka, Suita, Osaka 565-0871, Japan

2 Research Center for Precision Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871, Japan

* Corresponding authors emails: harry@cp.prec.eng.osaka-u.ac.jp, ueda@cp.prec.eng.osaka-u.ac.jp, morikawa@prec.eng.osaka-u.ac.jp

DOI10.24435/materialscloud:ka-x7 [version v1]

Publication date: Sep 05, 2023

How to cite this record

Harry Handoko Halim, Ryo Ueda, Yoshitada Morikawa, Machine learning molecular dynamics simulation of CO-driven formation of Cu clusters on the Cu(111) surface, Materials Cloud Archive 2023.136 (2023), https://doi.org/10.24435/materialscloud:ka-x7

Description

The behavior of adsorbate-induced surface transformation can be clearly understood given the mechanical aspects of such phenomenon are well described at the atomic level. In this study, we provide the atomic-level description on the formation of Cu clusters on the Cu(111) surface by performing set of molecular dynamics simulations driven by machine-learning force-field. The machine learning technique called Gaussian Process (GP), as implemented in FLARE v1.1.2 (https://github.com/mir-group/flare/tree/1.1.2) was used to construct the machine-learning force-field. The dynamics simulations were performed using LAMMPS v29Sept2021 (https://github.com/lammps/lammps/tree/stable_29Sep2021_update2). This archive contains some supplementary data including the validation structures and also the MGP potential used to drive the MD simulations in LAMMPS. Additionally, the GP potential (before mapping) containing the database of atomic environment is also made available. The simulations at 450 K–550 K show clusters are formed within a hundred of ns when the Cu surface is exposed with CO. On the other hand, no cluster is formed within the same time interval on the clean Cu surface even at 550 K, which signifies the importance of CO exposure to the surface transformation. The effect of temperature to the formation of clusters is also investigated. The CO-decorated Cu clusters ranging from dimer to hexamer are detected within a hundred of ns at 450 K. Lowering the temperature to 350 K does not result in the formation of clusters within a hundred ns due to the scarce detachments of adatom, while raising the temperature to 550 K results in the formation of more clusters, ranging from dimer to heptamer, but with shorter lifetimes. The clusters can be formed directly through instantaneous detachment of a group of step-atoms or indirectly by aggregation of wandering Cu monomers and smaller clusters on the surface terrace. The preference to the indirect mechanism is indicated by the higher frequency of its occurrence. More details on the procedures and results of the research can be found in the paper.

Materials Cloud sections using this data

Files

File name	Size	Description
validation_data_dft.xyz MD5md5:c470d470c4e08680d7677ef85f9ae806	1.6 MiB	The validation structures labelled with DFT forces. The file is in the format of xyz which can be conveniently processed using Atomic Simulation Environment (ASE) package.
validation_data_mgp.xyz MD5md5:c18a8630864f63e662adbc9f3c2abeca	1.6 MiB	The validation structures labelled with MGP forces. The file is in the format of xyz which can be conveniently processed using Atomic Simulation Environment (ASE) package.
lmp.mgp MD5md5:6cae032459b81c1420e4b539bb85046c	161.8 MiB	The mapped gaussian process force-field which is used to drive molecular dynamics simulation in LAMMPS.
gp_from_aimd_model.json MD5md5:53d0d233d5de60dee8b6c1120df4050a	4.2 GiB	The GP model which contains the training database, together with the hyper-parameter of gaussian process. This file is used to generate the MGP file.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning molecular dynamics simulation carbon monoxide Cu surface surface reconstruction