SImon Gramatte^1,2,3*, Vladyslav Turlo^1*

1 Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, Feuerwerkerstrasse 39, 3602 Thun, Switzerland

2 Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université Bourgogne Franche-Comté, 9 Avenue A. Savary, Dijon Cedex, France

3 Laboratory for Joining Technologies and Corrosion, Empa - Swiss Federal Laboratories for Materials Science and Technology, Ueberlandstrasse 129, 8600 Duebendorf, Switzerland

* Corresponding authors emails: simon.gramatte@empa.ch, Vladyslav.turlo@empa.ch

DOI10.24435/materialscloud:ya-3k [version v1]

Publication date: Sep 26, 2023

How to cite this record

SImon Gramatte, Vladyslav Turlo, Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab-initio database., Materials Cloud Archive 2023.147 (2023), https://doi.org/10.24435/materialscloud:ya-3k

Description

In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.

Materials Cloud sections using this data

Files

File name	Size	Description
am_Al2O3.zip MD5md5:9cfff9e0a25da83e803dd8d2812bd53c	10.1 MiB	See README.txt
README.txt MD5md5:68a87e92263dc8ad1fd49acf85ccabfd	1.3 KiB	description for am_Al2O3.zip

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.

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Keywords

machine learning molecular dynamics MARVEL/DD1