Identification of High-Dielectric Constant Compounds from Statistical Design

TL;DR

This study uses statistical optimization and neural networks to identify new high-dielectric materials from large databases, revealing three novel compounds with promising electronic properties for device applications.

Contribution

The paper introduces a novel cross-database screening approach combining statistical methods and neural networks to discover high-dielectric materials, including previously unexplored compounds.

Findings

Identified three new high-dielectric compounds with ε between 69 and 101.

Discovered four materials with moderate dielectric constants (20-40).

Validated thermodynamic stability of two new compounds via phase diagrams.

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 $<$ $\epsilon$ $<$ 101) and large band gaps (2.9$<$ $E_{\text{g}}$(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 (Eu$_5$SiCl$_6$O$_4$ 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$<$$\epsilon$$<$40) and band gaps (2.3$<$$E_{\text{g}}$(eV)$<$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.