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

• URL: http://arxiv.org/abs/2506.08073v1
• Date: Mon, 09 Jun 2025 17:58:27 GMT
• Title: Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
• Authors: Yu Liu, Utkarsh Pratiush, Kamyar Barakati, Hiroshi Funakubo, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Sergei V. Kalinin,
• Abstract summary: Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices.<n>Here, we introduce a multi-objective kernel-learning workflow that infers the microstructural rules governing switching behavior directly from imaging data.<n>While demonstrated for ferroelectric domain switching, our approach provides a powerful, generalizable tool for navigating complex, non-differentiable design spaces.
• Score: 2.5041807796023425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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