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Machine learning lattice parameters of monoclinic double perovskites
Author(s) -
Zhang Yun,
Xu Xiaojie
Publication year - 2021
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.26480
Subject(s) - monoclinic crystal system , lattice constant , ionic radius , lattice (music) , ionic bonding , octahedron , crystal structure , materials science , condensed matter physics , crystallography , chemistry , ion , physics , optics , diffraction , acoustics , organic chemistry
Abstract Monoclinic double perovskites, such as A 2 B′B″O 6 , exhibit a wide range of unique electronic, magnetic, and optical properties, depending on the alloying elements, ordering, and lattice distortion. Four lattice constants, a , b , c , and β , can vary due to the ionic radii of alloying elements, which represent the compression, expansion, and tilting of octahedra in the crystal structure. In the current study, we develop the Gaussian process regression model to shed light on relationships between effective ionic radii and lattice constants for monoclinic double perovskites. The model is highly accurate and stable, contributing to fast, low‐cost estimations of lattice constants.