most citedDisentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

1 citations · 1 across the 1 of their papers we have counts for

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1 paper

cond-mat.dis-nn2020★ 1 cited

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

Sergei V. Kalinin, James J. Steffes, Yongtao Liu +2

Domain switching pathways in ferroelectric materials visualized by dynamic Piezoresponse Force Microscopy (PFM) are explored via variational autoencoder (VAE), which simplifies the…