Machine learning modeling of lattice constants for half-Heusler alloys

• Published in: AIP advances Vol. 10; no. 4; pp. 045121 - 045121-10
• Main Authors: Zhang, Yun, Xu, Xiaojie
• Format: Journal Article
• Language: English
• 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

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...