Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films

Journal Article · 28 July 2022 · Advanced Materials

DOI: https://doi.org/10.1002/adma.202202814 · OSTI ID:2423994

Qin, Shuyu ^[1]; Guo, Yichen ^[1]; Kaliyev, Alibek T. ^[1]; Agar, Joshua C. ^[2]

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1. Lehigh Univ., Bethlehem, PA (United States)
2. Lehigh Univ., Bethlehem, PA (United States); Drexel Univ., Philadelphia, PA (United States)

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) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. Further, to improve the interpretability of the latent space, a variational Kullback–Leibler-divergency regularization is imposed . Finally, regularization scheduling of β 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
• Sponsoring Organization: National Science Foundation (NSF); USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 2423994
• Journal Information: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 47 Vol. 34; ISSN 0935-9648
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English