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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202202814
|2 doi
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|a pubmed24n1147.xml
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|a (DE-627)NLM344257126
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|a (NLM)35906007
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Qin, Shuyu
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Why it is Unfortunate that Linear Machine Learning "Works" so well in Electromechanical Switching of Ferroelectric Thin Films
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 24.11.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a 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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|a Journal Article
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|a deep learning
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|a dimensionality reduction
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| 650 |
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|a ferroelectric switching
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| 650 |
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|a machine learning
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| 650 |
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|a multimodal hyperspectral imaging
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| 650 |
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4 |
|a unsupervised learning
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| 700 |
1 |
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|a Guo, Yichen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kaliyev, Alibek T
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Agar, Joshua C
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 47 vom: 15. Nov., Seite e2202814
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:34
|g year:2022
|g number:47
|g day:15
|g month:11
|g pages:e2202814
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| 856 |
4 |
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|u http://dx.doi.org/10.1002/adma.202202814
|3 Volltext
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|d 34
|j 2022
|e 47
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