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Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

Nataliya Lopanitsyna1*, Guillaume Fraux1, Maximilian A. Springer2, Sandip De2, Michele Ceriotti1*

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: nataliya.lopanitsyna@epfl.ch, michele.ceriotti@epfl.ch
DOI10.24435/materialscloud:73-yn [version v1]

Publication date: Apr 05, 2023

How to cite this record

Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, Michele Ceriotti, Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25, Materials Cloud Archive 2023.57 (2023), https://doi.org/10.24435/materialscloud:73-yn


Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.

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File name Size Description
4.2 KiB This README describes the HEA25 dataset containing VASP outputs (HEA25.tar.gz), XYZ snapshots (HEA25.extxyz), and a Chemiscope file (HEA25.chemiscope.json.gz).
116.4 MiB an XYZ format data file containing the complete dataset of approximately 25,000 structures, consisting of FCC and BCC configurations for 25 d-group elements, accompanied by their respective energies, forces, and stress tensors computed using VASP.
Visualize on Chemiscope
11.2 MiB a data file that can be used to generate an interactive visualization of the training data and can be loaded on http://chemiscope.org.
10.0 GiB an archive containing some of the raw output files obtained during the calculations of the HEA25 dataset.


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

Preprint (In this reference, a comprehensive discussion on the construction of the dataset can be found.)


machine learning high-entropy alloys neural network potential alchemical compression MARVEL

Version history:

2023.57 (version v1) [This version] Apr 05, 2023 DOI10.24435/materialscloud:73-yn