Andrea Fantasia^1*, F. Rovaris¹, O. Abou El Kheir¹, A. Marzegalli¹, D. Lanzoni¹, L. Pessina¹, P. Xiao², C. Zhou³, L. Li³, G. Henkelman⁴, E. Scalise¹, F. Montalenti¹

1 Dept. of Materials Science, University of Milano-Bicocca, via R. Cozzi 55, Milano, Italy

2 Dept. of Physics & Atmospheric Science, Dalhousie University, 1453 Lord Dalhousie Drive, B3H 4R2, Halifax, NS, Canada

3 Dept. of Materials Science and Engineering, Southern University of Science and Technology, 1088 Xueyuan Avenue, 518055, Shenzhen, P.R. China

4 Dept. of Chemistry, The University of Texas at Austin, 105 East 24th Street STOP A5300, 78712, Austin, TX, USA

* Corresponding authors emails: a.fantasia1@campus.unimib.it

DOI10.24435/materialscloud:r2-qc [version v1]

Publication date: Apr 11, 2024

How to cite this record

Andrea Fantasia, F. Rovaris, O. Abou El Kheir, A. Marzegalli, D. Lanzoni, L. Pessina, P. Xiao, C. Zhou, L. Li, G. Henkelman, E. Scalise, F. Montalenti, A NN-Potential for phase transformations in Ge, Materials Cloud Archive 2024.55 (2024), https://doi.org/10.24435/materialscloud:r2-qc

Description

In a recent preprint, entitled: "Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in Germanium", we presented a novel Neural-Network (NN) interatomic potential for Ge. We recall that Ge phases different from the cubic-diamond one are of particular interest for applications. Hexagonal Ge, for instance, displays superior optical properties. It is therefore important to investigate how, exploiting pressure, Ge can be transformed into different allotropes. In order to build a potential tackling kinetics of pressure-induced phase transformations, several kinetic paths (mainly sampled using the solid-state Nudged Elastic Band method) were added to the database, following a suitable active-learning procedure. Energies, forces, and stressed relative to the various configurations were computed ab initio using VASP with the PBE functional. The NN potential was trained using the Deep Potential Molecular Dynamic package (DeePMDkit). The potential greatly reproduces the relative stability of several Ge phases and yields at least a semi-quantitative description of the energetics along complex phase-transformation paths. In the present archive, we provide the full potential for use in LAMMPS and ASE, together with the full database produced using VASP.

Materials Cloud sections using this data

Files

File name	Size	Description
allfiles.zip MD5md5:d5faa3ad7e44d8b797ff59d1d7ea66c8	314.4 MiB	upon unzipping the "allfiles" folder contains README.txt file with instruction

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External references

Keywords

neural network germanium phase-transitions DeePMD