Computational modeling of antiferroelectrics: fundamental mechanisms and predictive design

Antiferroelectric materials, characterized by their unique antipolar ordering and field-induced phase transitions, exhibit outstanding performance in energy storage, electrocaloric, and electromechanical applications. Due to the complexity of their microscopic mechanisms, computational modeling has become a key tool for understanding and designing such materials. Here, we systematically outline multiscale computational methods, including first-principles calculations, effective Hamiltonians, atomistic potentials, and phase-field simulations, which have elucidated the origin of antiferroelectricity, phase-transition dynamics, and domain evolution. We emphasize how computational simulations enable material optimization and design through strain engineering, size effects, doping control, and interface manipulation, and demonstrate their predictive value in enhancing energy-storage density, electrocaloric response, and strain-mediated performance. The integration of multi-scale computational approaches with artificial intelligence is rapidly emerging as an indispensable tool for both fundamental research and high-performance device design in antiferroelectrics. This review aims to provide researchers with an overview of current frontiers in simulations of antiferroelectric materials, thereby promoting computationally guided material exploration and encouraging further investigation into this complex and promising field.