Nataliya Lopanitsyna^1*, Guillaume Fraux¹, Maximilian A. Springer², 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: 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

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:10607febf27ef3efd677ab153a02a311	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).
HEA25.extxyz MD5md5:6c87c09e71a8ba490f7bbca648175ed6	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.
HEA25.chemiscope.json.gz MD5md5:1813e7b8b6974c3abbd18e1c33df65f1 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.
HEA25.tar.gz MD5md5:b8d734c451909cb312de5126fc8f8dfd	10.0 GiB	an archive containing some of the raw output files obtained during the calculations of the HEA25 dataset.

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 neural network potential alchemical compression MARVEL