Modeling of lattice parameters of cubic perovskite oxides and halides

• Published in: Heliyon Vol. 7; no. 7; p. e07601
• Main Authors: Zhang, Yun, Xu, Xiaojie
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2021; Elsevier
• Subjects: cations; Crystal structure; Halide; Machine learning; magnetism; normal distribution; Oxide; Perovskite; Oxide; Perovskite; Halide; Machine learning; Crystal structure
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2021.e07601

Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskite; Crystal structure; Oxide; Halide; Machine learning