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.
Keywords: electromechanical breakdown; energy storage; machine learning; multilayer ceramic capacitors; texture engineering.
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