High dielectric ternary oxides from crystal structure prediction and high-throughput screening

Abstract

The development of new high dielectric materials is essential for advancement in modern electronics. Oxides are generally regarded as the most promising class of high dielectric materials for industrial applications as they possess both high dielectric constants and large band gaps. Most previous researches on high dielectrics were limited to already known materials. In this study, we conducted an extensive search for high dielectrics over a set of ternary oxides by combining crystal structure prediction and density functional perturbation theory calculations.From this search, we adopted multiple stage screening to identify 440 new low-energy high dielectric materials. Among these materials, 33 were identified as potential high dielectrics favorable for modern device applications. Our research has opened an avenue to explore novel high dielectric materials by combining crystal structure prediction and high throughput screening.

Predicting the Onset of Metal–Insulator Transitions in Transition Metal Oxides—A First Step in Designing Neuromorphic Devices

“…The value of η entering eq can be determined from first-principles calculations of the oxygen vacancy formation energy (see Section and 3.5) and V p can be computed from the relation between the applied ( E t ) and induced ( E p ) field where E is the net electric field inside the material, ε 0 is the dielectric constant of vacuum, and P perm is the permanent polarization (which is zero in the case of a symmetric structure and we assume it to be independent from the applied potential); P ion and P electron are the ionic and electronic contributions to the polarization induced by the field E here, ε r is the zero-frequency limit of the dielectric constant of the material and it can be computed from first-principles. , For simplicity, we estimate ε r using ε r = βε ∞ , a relation established in a recent high-throughput study of oxide and fluoride cubic perovskites. The electronic dielectric constant ε ∞ is a quantity simpler to compute than ε r .…”

Identification of high-dielectric constant compounds from statistical design

“…The design and discovery of novel materials using statistical modeling has become an active research area [20][21][22] in recent times, largely attributed to the availability of such HT datasets. Recently, multiple studies have reported HT-generation of dielectric data and subsequent analysis 9,23,24 . For example, Morita et al reported 25 machine learning modeling of data from MP 11,12,15 to assess the reliability of the theoretical models currently available to describe the dielectric properties of crystals.…”

Identification of High-Dielectric Constant Compounds from Statistical Design

“…The design and discovery of novel materials using statistical modelling has become an active research area [20,21,22] in recent times, largely attributed to the availability of such HT datasets. Recently, multiple studies have reported HT-generation of dielectric data and subsequent analysis [9,23,24]. For example, Morita et al reported [25] machine learning modeling of data from MP [12,11,15] to assess the reliability of the theoretical models currently available to describe the dielectric properties of crystals.…”