Sequential piezoresponse force microscopy and the 'small-data' problem
authors Trivedi, H; Shvartsman, VV; Medeiros, MSA; Pullar, RC; Lupascu, DC
nationality International
abstract The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets. This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be 'small' in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised teaming technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution.
issn 2057-3960
year published 2018
volume 4
digital object identifier (doi) 10.1038/s41524-018-0084-9
web of science category Chemistry, Physical; Materials Science, Multidisciplinary
subject category Chemistry; Materials Science
unique article identifier WOS:000438372900001
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journal analysis (jcr 2019):
journal impact factor 9.341
5 year journal impact factor 11.282
category normalized journal impact factor percentile 87.913
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