Journal article
Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films
Advanced materials (Weinheim), v 34(47), pn/a
17 Oct 2022
PMID: 35906007
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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. 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.
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Details
- Title
- Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films
- Creators
- Shuyu Qin - Lehigh UniversityYichen Guo - Lehigh UniversityAlibek T. Kaliyev - Lehigh UniversityJoshua C. Agar - Drexel University, Mechanical Engineering and Mechanics
- Publication Details
- Advanced materials (Weinheim), v 34(47), pn/a
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- Oak Ridge National Laboratory ORAU University Partnerships Office of Science User Facility National Science Foundation (TRIPODS + X:RES‐1839234) Army Research Laboratory
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000868753900001
- Scopus ID
- 2-s2.0-85139958017
- Other Identifier
- 991021861298404721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Chemistry, Physical
- Materials Science, Multidisciplinary
- Nanoscience & Nanotechnology
- Physics, Applied
- Physics, Condensed Matter