Revealing ferroelectric switching character using deep recurrent neural networks

Joshua C. Agar, Brett Naul, Shishir Pandya, Stefan van der Walt, Joshua Maher, Yao Ren, Long Qing Chen, Sergei V. Kalinin, Rama K. Vasudevan, Ye Cao, Joshua S. Bloom, Lane W. Martin

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material’s physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.

Original languageEnglish (US)
Article number4809
JournalNature communications
Issue number1
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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