FePt 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 (FePt) binary alloy with body-centered cubic (BCC) structure. The configurations span all the compositions from 0%Fe - 100% Pt through 100%Fe - 0% Pt. 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 â€¢ Â© 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)

Other contributing organizations

Oak Ridge Leadership Facility (OLCF)

Sponsoring organization

Office of Science (SC)

Details

DOI

10.13139/OLCF/1762742

Release date

February 15, 2021

Dataset

Dataset type

ND Numeric Data

Acknowledgements

Users should acknowledge the OLCF in all publications and presentations that speak to work performed on OLCF resources:

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Keywords

machine learning,
alloy,
first principles