Somayeh Faraji^1*, S. Alireza Ghasemi^1*, Behnam Parsaeifard^2*, Stefan Goedecker^2*

1 Physics department, Institute for Advanced Studies in Basic Sciences, P.O. Box 45195-1159, Zanjan, Iran

2 Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland

* Corresponding authors emails: sfaraji86@gmail.com, alirezagh76@gmail.com, behnam.parsaeifard@unibas.ch, stefan.goedecker@unibas.ch

DOI10.24435/materialscloud:2020.0018/v1 [version v1]

Publication date: Jan 31, 2020

How to cite this record

Somayeh Faraji, S. Alireza Ghasemi, Behnam Parsaeifard, Stefan Goedecker, Surface reconstructions and premelting of the (100) CaF2 surface, Materials Cloud Archive 2020.0018/v1 (2020), https://doi.org/10.24435/materialscloud:2020.0018/v1

Description

In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method (MHM) coupled to a machine-learning interatomic potential, that is based on a charge equilibration scheme steered by a neural network (CENT). The combination of these powerful methods revealed about 80 different morphologies for the (100) surface with very similar surface formation energies differing by not more than 0.3 J m−2. To take into account the effect of temperature on the dynamics of this surface as well as to study the solid–liquid transformation, molecular dynamics simulations were carried out in the canonical (NVT) ensemble. By analyzing the atomic mean-square displacements (MSD) of the surface layer in the temperature range of 300–1200 K, it was found that in the surface region the F sublattice is less stable and more diffusive than the Ca sublattice. Based on these results we demonstrate that not only a bulk system, but also a surface can exhibit a sublattice premelting that leads to superionicity. Both the surface sublattice premelting and surface premelting occur at temperatures considerably lower than the bulk values. The complex behaviour of the (100) surface is contrasted with the simpler behavior of other low index crystallographic surfaces.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:eecae50313b51296489876f2ff0593d6	7.2 KiB	README.txt
CaF2_001_surfaces_reconstructions.tar.gz MD5md5:11c8646eb2be89a20fe22feb66bfc6f7	1.8 MiB	All the structures are relaxed with the BigDFT code using the PBE functional and the pseudopotentials contained in the psppar* files. The input parameters for the BigDFT calculations are in the input.yaml 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

MARVEL surface reconstructions machine learning SNSF MARVEL/DD1 Minima Hopping charge equilibration scheme