Open Access

REVIEW

Review on Phase-Field Modeling of Fracture in Ferroelectric Materials

Shuai Wang^1,*, Ke Han¹, Min Yi^2,*

1 Hangzhou International Innovation Institute of Beihang University, Hangzhou, China
2 State Key Laboratory of Mechanics and Control for Aerospace Structures & Institute for Frontier Science & College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China

* Corresponding Authors: Shuai Wang. Email: ; Min Yi. Email:

Computer Modeling in Engineering & Sciences 2026, 147(3), 4 https://doi.org/10.32604/cmes.2026.082535

Received 17 March 2026; Accepted 12 May 2026; Issue published 30 June 2026

Abstract

Ferroelectric materials, integral to modern sensors, actuators, and transducers, exhibit complex fracture behavior under coupled electromechanical loading due to the intrinsic interplay between cracks, domain structures, and microstructural features. Linear piezoelectric fracture mechanics provides a foundational framework but fails to capture nonlinearities induced by domain switching and microstructure. This review synthesizes advances in computational modeling of ferroelectric fracture, with a focus on the unifying capabilities of the phase-field method (PFM). We first establish the fundamentals, including fracture toughness anisotropy and the crack-tip flexoelectric effect. We then critically assess traditional approaches like cohesive zone models and the extended finite element method, highlighting their limitations in handling arbitrary crack paths and complex microstructure evolution. The core of the review details how PFM has emerged as a transformative paradigm, enabling the simulation of diffuse crack propagation seamlessly coupled with ferroelectric domain dynamics within a single variational framework. We systematically examine recent progress in applying this framework to model fracture coupled with explicit microstructure (e.g., grains and domain walls), dielectric breakdown, fatigue under cyclic loading, and the integration of machine learning for model acceleration and inverse design. The review concludes by identifying persistent challenges, such as reconciling crack-face boundary conditions and bridging atomic-scale mechanisms with polycrystal-scale failure, and outlines future research directions toward predictive, multiscale, and experimentally validated models for designing reliable next-generation ferroelectric devices.

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Keywords

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