Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by polarization dynamics and domain kinetics, making a microscopic understanding of these processes and precise atomic-level control of polarization domains key challenges for next generation ferroelectric electronics. Due to the limitations of the characterization technology with atomic-level in experiment, high-precision atomic simulations become important. First-principles calculations are inherently limited in accessible length and time scales, making it difficult to capture the complex features of dynamic processes. Machine-learning molecular dynamics (MLMD) offers a compelling solution by encoding quantum-mechanical accuracy into force fields, thereby enabling large-scale dynamic simulations with near first-principles fidelity. This Perspective highlights the advantages of MLMD for simulating polarization switching, domain nucleation and migration, topological polar textures and curvature-driven ferroelectric phenomena, while providing a systematic overview of recent progress in these areas. We further discuss methodological challenges that limit predictive capability, including long-range electrostatics, coupled lattice-spin degrees of freedom in multiferroics, and data-efficient pre-training of large atomistic models. Corresponding advances in long-range-aware force fields, spin-dependent machine-learning models, and large-scale pre-training are expected to move MLMD toward a genuinely predictive framework for the design of ferroelectric and multiferroic materials.