Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods

Scott R. Broderick, Aakash Kumar, Adedapo A. Oni, James M. LeBeau, Susan B. Sinnott, Krishna Rajan

Research output: Contribution to journalArticle

Abstract

We show how one may extract spectral features from the density of states (DOS) of L12-Ni3Al alloys that can serve as signatures or electronic “fingerprints” which capture the correlation between site occupancy of dopants and elastic properties. Based on this correlation, we have developed a computational approach for rapidly identifying the impact of the selection of dopant chemistries on bulk moduli of intermetallics. Our results show for example that Cr preferentially occupies the Al site in Ni3Al which is confirmed by scanning transmission electron microscopy (STEM) energy dispersed X-ray spectroscopy (EDS) analysis. We further show that this preference is due to a sensitivity of Cr to the DOS at −1.7 and 0.2 eV relative to the Fermi energy. In terms of similarity in chemistry-property correlations, we find Cr has a similar effect to Ce when occupying an Al site, while Cr occupying a Ni site has similar correlation as La on a Ni site. This logic can be utilized in targeted design of new alloy chemistries based on similar property correlations and for targeted DOS modification.

Original languageEnglish (US)
Pages (from-to)8-14
Number of pages7
JournalComputational Condensed Matter
Volume14
DOIs
StatePublished - Mar 1 2018

Fingerprint

learning
Intermetallics
intermetallics
Doping (additives)
chemistry
X ray spectroscopy
Fermi level
Energy dispersive spectroscopy
Elastic moduli
Transmission electron microscopy
Scanning electron microscopy
bulk modulus
logic
elastic properties
signatures
transmission electron microscopy
scanning electron microscopy
energy
sensitivity
electronics

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Materials Science (miscellaneous)
  • Condensed Matter Physics
  • Materials Chemistry

Cite this

Broderick, Scott R. ; Kumar, Aakash ; Oni, Adedapo A. ; LeBeau, James M. ; Sinnott, Susan B. ; Rajan, Krishna. / Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods. In: Computational Condensed Matter. 2018 ; Vol. 14. pp. 8-14.
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Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods. / Broderick, Scott R.; Kumar, Aakash; Oni, Adedapo A.; LeBeau, James M.; Sinnott, Susan B.; Rajan, Krishna.

In: Computational Condensed Matter, Vol. 14, 01.03.2018, p. 8-14.

Research output: Contribution to journalArticle

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