Arslan Mazitov^1*, Maximilian A. Springer², Nataliya Lopanitsyna¹, Guillaume Fraux¹, Sandip De², Michele Ceriotti^1*

1 Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2 BASF SE, Carl-Bosch-Straße 38, 67056 Ludwigshafen, Germany

* Corresponding authors emails: arslan.mazitov@epfl.ch, michele.ceriotti@epfl.ch

DOI10.24435/materialscloud:ps-20 [version v1]

Publication date: Oct 23, 2023

How to cite this record

Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna, Guillaume Fraux, Sandip De, Michele Ceriotti, Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S, Materials Cloud Archive 2023.160 (2023), https://doi.org/10.24435/materialscloud:ps-20

Description

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA. In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs. We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:42199073093e01ec0c39ddf6483efccb	2.6 KiB	This README describes the HEA25S dataset containing compressed XYZ configurations (data.zip), HEA25-4-NN and HEA25S-4-NN models in the PyTorch model dict format (models.zip), and VASP settings used for calculations (vasp_settings.zip)
data.zip MD5md5:b3d9dc10f8d28a31954e220b70eb86f3	29.7 MiB	A zipped folder with XYZ files of the HEA25S dataset, containing 5 different classes of HEA data used in the study, spliced by the train, validation and test sets
models.zip MD5md5:594636e633280755c1569f4a3f1ecc1b	2.5 MiB	A zipped folder with HEA25-4-NN and HEA25S-4-NN models in the PyTorch model dict format
vasp_settings.zip MD5md5:15ddf00bbac033a7f8f202f38a694102	1.3 KiB	A zipped folder with the VASP INCAR 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 high-entropy alloys surface segregation atomistic modeling density-functional theory alchemical compression MARVEL COSMO