Progress on Ferroelectric Materials

Dear Colleagues,

The field of ferroelectric materials has undergone remarkable evolution and expansion in recent years, moving far beyond the traditional oxide perovskite ferroelectrics (such as BaTiO₃, PbTiO₃, and KNbO₃) that dominated the landscape for decades. A variety of exciting new classes of ferroelectric systems have emerged, including low-dimensional ferroelectrics (van der Waals ferroelectrics, two-dimensional ferroelectrics, moiré ferroelectrics, and sliding ferroelectrics); nitride perovskites (e.g., CeTaN₃); Hf-based ferroelectrics (e.g., HfO₂); and wurtzite ferroelectrics (e.g., Al_1-xSc_xN). These novel systems exhibit robust polarization at the nanoscale, compatibility with silicon-based technologies, unconventional switching mechanisms, exotic domain-wall behaviors and topological structures, and unprecedented tunability, opening pathways to next-generation memory devices, neuromorphic computing architectures, and energy-harvesting technologies.

In parallel, machine learning interatomic potentials (MLIPs, such as MACE, NEP, GAP, and M3GNet) and related data-driven approaches are revolutionizing the modeling and understanding of ferroelectric phenomena. High-fidelity ML force fields now enable accurate, large-scale, and long-timescale simulations of polarization switching, domain-wall dynamics, topological defect evolution, phase transitions, and electromechanical responses—capabilities that were previously inaccessible to conventional density functional theory or classical molecular dynamics. Coupled with active learning, uncertainty quantification, and interpretable model frameworks, these approaches are delivering unprecedented mechanistic insights and accelerating the discovery and optimization of ferroelectric materials with targeted functionalities.

This Special Issue, entitled “Progress on Ferroelectric Materials”, aims to showcase the latest advancements at both the experimental and computational frontiers of ferroelectricity. Topics of interest include, but are not limited to, the following:

• Low-dimensional and van der Waals ferroelectrics.
• Moiré and sliding ferroelectrics.
• Nitride perovskites and wurtzite ferroelectrics.
• Ferroelectricity in HfO₂-based systems.
• Topological structures, domain walls, and polar textures.
• Machine-learning-assisted modeling of ferroelectric switching and domain dynamics.
• Machine learning interatomic potentials for large-scale ferroelectric simulations.
• Inverse design and high-throughput screening of new ferroelectrics.
• Ferroelectricity in quantum materials and under extreme conditions.
• Applications in memory, neuromorphic computing, sensors, and energy devices.

We cordially invite leading researchers and rising talents in the field to contribute original research articles, reviews, and perspectives that highlight state-of-the-art advances and outline future directions in ferroelectric materials science.

Dr. Lixiang Rao
Dr. Gang Tang
Guest Editors

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• artificial intelligence
• machine learning
• data-driven materials discovery
• automated experimentation
• inverse design
• materials databases