Journal
ADVANCED MATERIALS
Volume 34, Issue 47, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202202814
Keywords
deep learning; dimensionality reduction; ferroelectric switching; machine learning; multimodal hyperspectral imaging; unsupervised learning
Categories
Funding
- National Science Foundation [TRIPODS + X:RES-1839234]
- DOE Data Reduction for Science award Real-Time Data Reduction Codesign at the Extreme Edge for Science
- Army Research Laboratory Collaborative for Hierarchical Agile and Responsive Materials
- ORAU University Partnerships
- Lehigh Presidential Initiative on Nanohuman Interfaces
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This study utilizes machine learning methods to analyze electromechanical switching mechanisms in materials spectroscopy and overcomes common pitfalls. By using multiple experimental datasets and specific algorithms, researchers successfully achieve automatic detection of complex switching mechanisms in ferroelectric materials.
Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called better, faster, and less-biased ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) beta-variational autoencoders (beta-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback-Leibler-divergency regularization is imposed . Finally, regularization scheduling of beta as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.
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