Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM

Abstract

Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties - such as polarization directions essential for understanding functional properties of ferroelectrics - remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to automate the detection of polarization directions from 4D-STEM diffraction patterns in ferroelectric potassium sodium niobate. While models trained on synthetic data achieve high accuracy on idealized synthetic diffraction patterns of equivalent thickness, the domain gap between simulation and experiment remains a critical barrier to real-world deployment. In this context, a custom made prototype representation training regime and PCA-based methods, combined with data augmentation and filtering, can better bridge this gap. Error analysis reveals periodic missclassification patterns, indicating that not all diffraction patterns carry enough information for a successful classification. Additionally, our qualitative analysis demonstrates that irregularities in the model's prediction patterns correlate with defects in the crystal structure, suggesting that supervised models could be used for detecting structural defects. These findings guide the development of robust, transferable machine learning tools for electron microscopy analysis.

Figures (8)

• Figure 1: (a) Crystal structure of orthorhombic KNN viewed along the $[100]_{\rm pc}$ zone axis with a graph showing 8 possible Nb displacement directions, and examples of the corresponding (b) simulated and (c) experimental diffraction patterns.
• Figure 2: Methodologies used. The upper part shows standard classification approaches and regression with mapping to classes, while the lower part shows the prototype-representation-based approach.
• Figure 3: Prediction distribution on the synthetic test datasets for the Conv (Proto) and PCA methods with standard deviation across 5 seeds.
• Figure 4: Prediction distribution on the experimental test datasets for the Conv (Proto) and PCA methods with standard deviation across 5 seeds.
• Figure 5: Polarization classification (different color per each class) for each diffraction pattern in a $128\times128$ structure across all synthetic structures. We take a majority vote across five seeds for each prediction.