arXiv subjects

Dongyu Bai

Publications and source records attributed to Dongyu Bai.

3 recordsLinked to original sources

arXiv2026

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 pretraining are expected to move MLMD toward a genuinely predictive framework for the design of ferroelectric and multiferroic materials.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelectric Domains and Evolution Dynamics in Twisted CuInP2S6 Bilayers

Polar domains and their manipulation-particularly the creation and dynamic control-have garnered significant attention, owing to their rich physics and promising applications in digital memory devices. In this work, using density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations, we demonstrate that polar domains can be created and manipulated in twisted bilayers of ferroelectric CuInP2S6, as a result of interfacial ferroelectric (antiferroelectric) coupling in AA (AB) stacked region. Unlike the topological polar vortex and skyrmions observed in superlattices of (PbTiO3)n/(SrTiO3)n and sliding bilayers of BN and MoS2, the underlying mechanism of polar domain formation in this system arises from stacking-dependent energy barriers for ferroelectric switching and variations in switching speeds under thermal perturbations. Notably, the thermal stability and polarization lifetimes are highly sensitive to twist angles and temperature, and can be further manipulated by external electric fields and strain. Through multi-scale simulations, our study provides a novel approach to exploring how twist angles influence domain evolution and underscores the potential for controlling local polarization in ferroelectric materials via rotational manipulation.

cond-mat.mtrl-sci↗

arXiv2023

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First Principle and Deep Learning Molecular Dynamics Simulations

Complex strain status can exist in 2D materials during their synthesis process, resulting in significant impacts on the physical and chemical properties. Despite their prevalence in experiments, their influence on the material properties and the corresponding mechanism are often understudied due to the lack of effective simulation methods. In this work, we investigated the effects of bending, rippling, and bubbling on the ferroelectric domains in In2Se3 monolayer by density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations. The analysis of the tube model shows that bending deformation imparts asymmetry into the system, and the polarization direction tends to orient towards the tensile side, which has a lower energy state than the opposite polarization direction. The energy barrier for polarization switching can be reduced by compressive strain according DFT results. The dynamics of the polarization switching is investigated by the DLMD simulations. The influence of curvature and temperature on the switching time follows the Arrhenius-style function. For the complex strain status in the rippling and bubbling model, the lifetime of the local transient polarization is analyzed by the autocorrelation function, and the size of the stable polarization domain is identified. Local curvature and temperature can influence the local polarization dynamics following the proposed Arrhenius-style equation. Through cross-scale simulations, this study demonstrates the capability of deep-learning potentials in simulating polarization for ferroelectric materials. It further reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering.

cond-mat.mtrl-sci↗