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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics.
Συγγραφείς:	Bai D; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Queensland, Australia., He R; Zhejiang Key Laboratory of Magnetic Materials and Applications, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China., Liu J; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Queensland, Australia., Kou L; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
Πηγή:	Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2026 Jun 18, pp. e76203. Date of Electronic Publication: 2026 Jun 18.
Publication Model:	Ahead of Print
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE
Imprint Name(s):	Original Publication: Weinheim : WILEY-VCH, [2014]-
Περίληψη:	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. (© 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)
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Grant Information:	Queensland University of Technology Postgraduate Research Award; DP230101904 ARC Discovery Project; DP240103085 ARC Discovery Project; 12574108 Natural Science Foundation of China; DG26A040001 Zhejiang Provincial Natural Science Foundation of China
Contributed Indexing:	Keywords: domain wall motion; ferroelectric materials; machine learning molecular dynamics; polarization switching; topological polar textures
Entry Date(s):	Date Created: 20260618 Latest Revision: 20260726
Update Code:	20260726
PubMed Central ID:	PMC13336982
DOI:	10.1002/advs.76203
PMID:	42314055
Βάση Δεδομένων:	MEDLINE

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ISSN:	2198-3844
DOI:	10.1002/advs.76203