ABSTRACT
Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm-3 at an electric field of 5104 kV cm-1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.
ABSTRACT
Dielectric capacitors, characterized by ultra-high power densities, are considered as fundamental energy storage components in electronic and electrical systems. However, synergistically improving energy densities and efficiencies remains a daunting challenge. Understanding the role of polarity heterogeneity at the nanoscale in determining polarization response is crucial to the domain engineering of high-performance dielectrics. Here, a bidirectional design with phase-field simulation and machine learning is performed to forward reveal the structure-property relationship and reversely optimize polarity heterogeneity to improve energy storage performance. Taking BiFeO3-based dielectrics as typical systems, this work establishes the mapping diagrams of energy density and efficiency dependence on the volume fraction, size and configuration of polar regions. Assisted by CatBoost and Wolf Pack algorithms, this work analyzes the contributions of geometric factors and intrinsic features and find that nanopillar-like polar regions show great potential in achieving both high polarization intensity and fast dipole switching. Finally, a maximal energy density of 188 J cm-3 with efficiency above 95% at 8 MV cm-1 is obtained in BiFeO3-Al2O3 systems. This work provides a general method to study the influence of local polar heterogeneity on polarization behaviors and proposes effective strategies to enhance energy storage performance by tuning polarity heterogeneity.
ABSTRACT
Understanding the electromechanical breakdown mechanisms of polycrystalline ceramics is critical to texture engineering for high-energy-density dielectric ceramics. Here, an electromechanical breakdown model is developed to fundamentally understand the electrostrictive effect on the breakdown behavior of textured ceramics. Taking the Na0.5 Bi0.5 TiO3 -Sr0.7 Bi0.2 TiO3 ceramic as an example, it is found that the breakdown process significantly depends on the local electric/strain energy distributions in polycrystalline ceramics, and reasonable texture design could greatly alleviate electromechanical breakdown. Then, high-throughput simulations are performed to establish the mapping relationship between the breakdown strength and different intrinsic/extrinsic variables. Finally, machine learning is conducted on the database from the high-throughput simulations to obtain the mathematical expression for semi-quantitatively predicting the breakdown strength, based on which some basic principles of texture design are proposed. The present work provides a computational understanding of the electromechanical breakdown behavior in textured ceramics and is expected to stimulate more theoretical and experimental efforts in designing textured ceramics with reliable electromechanical performances.
