Identification of High-Dielectric Constant Compounds from Statistical Design

Abstract

The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < < 101) and large band gaps (2.9< Eg(eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu5SiCl6O4 and HoClO), and are shown to be thermodynamically stable against common semiconductors via phase-diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20< <40) and band gaps (2.3<Eg(eV)<2.7). While the ANN training data is obtained from Materials Project, the search-space consists of materials from Open Quantum Materials Database (OQMD) -demonstrating a successful implementation of cross-database materials design. Overall, we report dielectric properties of 17 materials calculated using ab-initio calculations, that were selected in our design workflow. The dielectric materials with high dielectric properties predicted in this work open up further experimental research opportunities.