Machine learning modeling of lattice constants for half-Heusler alloys

The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements...

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Bibliographic Details
Published inAIP advances Vol. 10; no. 4; pp. 045121 - 045121-10
Main Authors Zhang, Yun, Xu, Xiaojie
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 01.04.2020
AIP Publishing LLC
Subjects
Alloy development
Alloying elements
Aluminum
Antimony
Electronegativity
Gaussian process
Germanium
Heusler alloys
Lattice parameters
Machine learning
Manganese
Modelling
Nickel
Regression models
Silicon
Statistical analysis
Statistical methods
Tin
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Summary:The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements of Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, and Sb, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants of half-Heusler compounds.
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ISSN:2158-3226
2158-3226
DOI:10.1063/5.0002448