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arXiv 2506.08073cond-mat.mtrl-scicond-mat.mes-hallcs.AIcs.LG

Pareto前沿上的畴切换:自动化压电力显微镜中的多目标深度核学习

Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy

University of Tennessee(田纳西大学);Institute of Science Tokyo(东京科学大学);University of California, Berkeley(加州大学伯克利分校);Rice University(莱斯大学);Pacific Northwest National Laboratory(太平洋西北国家实验室)

Yu Liu, Utkarsh Pratiush, Kamyar Barakati, Hiroshi Funakubo, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Sergei V. Kalinin

AI总结:

提出多目标深度核学习工作流,从高分辨PFM成像中学习铁电畴壁、缺陷与极化切换动力学的关系,并将抽象奖励映射为可解释微结构描述符,实现主动学习与机理发现。

摘要:

铁电极化切换构成了广泛材料和器件功能性能的基础,然而其对复杂局域微结构特征的依赖,使得通过手动或基于网格的光谱测量进行系统性探索变得不切实际。本文提出一种多目标核学习工作流,可直接从高分辨成像数据推断控制切换行为的微结构规则。该框架应用于自动化压电力显微镜(PFM)实验,能够高效识别畴壁构型与局域切换动力学之间的关键关系,揭示特定畴壁几何形状和缺陷分布如何调控极化反转。实验后分析将切换难易程度和畴对称性等抽象奖励函数投影到包括畴构型及与边界接近程度在内的物理可解释描述符上。这不仅实现了高通量主动学习,还提供了对切换现象微结构控制的机理认识。尽管该方法在铁电畴切换中得到演示,但它为在复杂、不可微设计空间中导航提供了强大且可推广的工具,应用范围从分子发现中的结构–性能关联到跨多种成像模态的组合优化。

英文摘要:

Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features renders systematic exploration by manual or grid-based spectroscopic measurements impractical. Here, we introduce a multi-objective kernel-learning workflow that infers the microstructural rules governing switching behavior directly from high-resolution imaging data. Applied to automated piezoresponse force microscopy (PFM) experiments, our framework efficiently identifies the key relationships between domain-wall configurations and local switching kinetics, revealing how specific wall geometries and defect distributions modulate polarization reversal. Post-experiment analysis projects abstract reward functions, such as switching ease and domain symmetry, onto physically interpretable descriptors including domain configuration and proximity to boundaries. This enables not only high-throughput active learning, but also mechanistic insight into the microstructural control of switching phenomena. While demonstrated for ferroelectric domain switching, our approach provides a powerful, generalizable tool for navigating complex, non-differentiable design spaces, from structure-property correlations in molecular discovery to combinatorial optimization across diverse imaging modalities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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