Abstract
ABSTRACT Dielectric materials that can realize downsizing and higher performance in electric devices are in demand. Perovskite-type materials of the form ABO3 are potential candidates. However, because of the numerous conceivable compositions of perovskite-type oxides, finding the best composition is technically difficult. To obtain a reasonable guideline for material design, we aim to clarify the relationship between the dielectric constants and other physical and chemical properties of perovskite-type oxides using first-principles density functional theory (DFT) and partial least-squares regression analysis. The more important factors affecting the dielectric constants are predicted based on variable importance in projection (VIP) scores. The dielectric constant strongly correlates with the ionicity of the B cations and the density of states of the conduction bands of the B cations.
Highlights
In recent years, increasing attention has been paid toward simulating informatics-aided materials
We first present the results of the dielectric constants of the perovskite-type oxides obtained using the firstprinciples calculations with the density functional perturbation theory (DFPT)
No strong correlations exist between the dielectric constant and the two descriptors, we investigated the relationships between dielectric constant and two descriptors among the six explanatory variables a − f
Summary
In recent years, increasing attention has been paid toward simulating informatics-aided materials. Materials informatics can substantially contribute to data mining by clarifying the relationships, i.e. the so-called quantitative structure-property relationships (QSPRs), between the structures and properties of functional materials [10]. In this case, regression analysis methods, such as partial least-squares (PLS) regression, principal component analysis (PCA), support vector regression (SVR), and least absolute shrinkage and selection operator (LASSO) regression, are often used for building regression models for molecules and crystal structures [11,12,13,14,15,16,17,18,19]. Clarifying the descriptors indicating the QSPRs will accelerate the design of functional materials
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