FePt binary alloy with 32 atoms - LSMS-3 data

Pasini, Massimiliano Lupo; Eisenbach, Markus

doi:10.13139/OLCF/1762742

Title: FePt binary alloy with 32 atoms - LSMS-3 data

Dataset
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Abstract

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

Authors:

Pasini, Massimiliano Lupo; Eisenbach, Markus

ORNL-OLCF

Publication Date:: Mon Feb 15 04:00:00 UTC 2021

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC)

Collaborations:: Oak Ridge Leadership Facility (OLCF)

Subject:: 36 MATERIALS SCIENCE; 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; machine learning, alloy, first principles

OSTI Identifier:: 1762742

DOI:: https://doi.org/10.13139/OLCF/1762742

Citation Formats


                    Pasini, Massimiliano Lupo, and Eisenbach, Markus. FePt binary alloy with 32 atoms - LSMS-3 data.  United States: N. p., 2021. 
        Web.  doi:10.13139/OLCF/1762742.

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                    Pasini, Massimiliano Lupo, & Eisenbach, Markus. FePt binary alloy with 32 atoms - LSMS-3 data.  United States.  doi:https://doi.org/10.13139/OLCF/1762742

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                    Pasini, Massimiliano Lupo, and Eisenbach, Markus. 2021.  
"FePt binary alloy with 32 atoms - LSMS-3 data".  United States.  doi:https://doi.org/10.13139/OLCF/1762742.  https://www.osti.gov/servlets/purl/1762742. Pub date:Mon Feb 15 04:00:00 UTC 2021

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@article{osti_1762742,

  title        = {FePt binary alloy with 32 atoms - LSMS-3 data},

  author       = {Pasini, Massimiliano Lupo and Eisenbach, Markus},

  abstractNote = {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},

  doi          = {10.13139/OLCF/1762742},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Feb 15 04:00:00 UTC 2021},

  month        = {Mon Feb 15 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.13139/OLCF/1762742

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