CuAu binary alloy with 32 atoms - LSMS-3 data

Lupo Pasini, Massimiliano | Oak Ridge National Laboratory
Eisenbach, Markus | Oak Ridge National Laboratory

Overview

Description

This dataset contains the estimate of atomic charge density, atomic magnetic moment and total energy for 32,000 configurations of the iron-platinum (CuAu) binary alloy with face-centered cubic (FCC) structure. The configurations span all the compositions from 0%Cu - 100% Au through 100%Cu - 0% Au. The results have been produced running ab-initio density functional theory (DFT) calculations with the LSMS-3 code on OLCF supercomputer Titan. LSMS-3 GitHub repository: https://github.com/mstsuite/lsms Deep Learning research papers published with results based on this dataset: Fast and stable deep-learning predictions of material properties for solid solution alloys Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin, Jiaxin Zhang, Kipton Barros and Markus Eisenbach Published 14 December 2020. 2020 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 33, Number 8 Citation Massimiliano Lupo Pasini et al 2021 J. Phys.: Condens. Matter 33 084005 https://iopscience.iop.org/article/10.1088/1361-648X/abcb10

Funding resources

DOE contract number

DE-AC05-00OR22725

Originating research organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring organization

Office of Science (SC)

Details

DOI

10.13139/OLCF/1765349

Release date

February 15, 2021

Dataset

Dataset type

ND Numeric Data

Acknowledgements

Papers using this dataset are requested to include the following text in their acknowledgements:

*Support for 10.13139/OLCF/1765349 is provided by the U.S. Department of Energy, project MAT020 under Contract DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.

Keywords

machine learning,
alloy,
first principles