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Machine learning the lattice constant of cubic pyrochlore compounds
Author(s) -
Zhang Yun,
Xu Xiaojie
Publication year - 2021
Publication title -
international journal of applied ceramic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.4
H-Index - 57
eISSN - 1744-7402
pISSN - 1546-542X
DOI - 10.1111/ijac.13709
Subject(s) - electronegativity , ionic radius , pyrochlore , lattice constant , materials science , ternary operation , ionic bonding , lattice (music) , stoichiometry , doping , condensed matter physics , ion , thermodynamics , chemistry , computer science , physics , optics , quantum mechanics , diffraction , optoelectronics , acoustics , programming language , phase (matter)
Pyrochlores, with a general formula A 2 B 2 X 7 , are promising candidates in many potential applications due to their wide varieties of physical properties. Different combinations of cations and anions in the crystal structure enable the tailoring of their properties and functionalities. For cubic pyrochlores, the lattice constant, a, is an integrated result of stoichiometry, ionic radii, and electronegativities of alloying elements. It also has significant impacts on stabilities, electronic structures, ionic conductivities, and thus performance of materials. Here, we develop the Gaussian process regression model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic pyrochlores. The dataset includes ternary pyrochlores and mixed pyrochlores with co‐doping and multi‐doping situations. One hundred and thirty‐nine samples with lattice constants between 9.287 and 11.150 Å are examined. The model is highly stable and accurate that contributes to efficient and low‐cost lattice constant estimations.