Harry Handoko Halim^1*, Yoshitada Morikawa^1,2,3*

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

2 Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Goryo-Ohara, Nishikyo-ku, Kyoto 615- 8245, Japan

3 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, morikawa@prec.eng.osaka-u.ac.jp

DOI10.24435/materialscloud:gh-wt [version v1]

Publication date: Jun 15, 2022

How to cite this record

Harry Handoko Halim, Yoshitada Morikawa, The mapped gaussian process (MGP) force-field of Cu-Zn surface alloy, Materials Cloud Archive 2022.79 (2022), https://doi.org/10.24435/materialscloud:gh-wt

Description

The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning technique called Gaussian Process as implemented in FLARE package (https://github.com/mir-group/flare). Active and on-the-fly learning were employed to build the database efficiently. The simulation reveals atomistic details of the alloying process, i.e., the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation.

Materials Cloud sections using this data

Files

File name	Size	Description
CuZn_surface.mgp MD5md5:958ee2cd0957098da436e59abaa34e51	1.7 MiB	This force-field is generated by using FLARE package (https://github.com/mir-group/flare) and can be used in MD simulation package LAMMPS.

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

Cu-Zn surface machine learning molecular dynamics first principles