Abstract
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 language | English |
|---|---|
| Article number | 4809 |
| Journal | Nature Communications |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2019 |
Bibliographical note
Publisher Copyright:© 2019, The Author(s).
Funding
The band-excitation PFM portion of this research was conducted at the Center for Nanophase Materials Sciences, which also provided support (R.K.V., S.V.K.), and is a US DOE Office of Science User Facility. The authors acknowledge fruitful conversations with Tess Schmidt. J.C.A, J.M. and L.W.M. acknowledge primary support of the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05-CH11231 (Materials Project program KC23MP) for the development of advanced functional materials and data-driven approaches to materials study. For work at Lehigh, J.C.A. acknowledges support from the National Science Foundation under grant TRIPODS + X:RES-1839234, and the Nano/Human Interfaces Presidential Initiative, the Institute for Functional Materials and Devices, and the Institute for Intelligent Systems and Computation at Lehigh University. B.N. and J.S.B. acknowledge the support of the Gordon and Betty Moore Foundation Data-Driven Discovery, the National Science Foundation BIGDATA grant number 1251274, and the Berkeley Institute of Data Science. S.P. acknowledges the support of the Army Research Office under grant W911NF-14-1-0104. S.v.d.W. acknowledges support by the Gordon and Betty Moore Foundation through Grant GBMF3834 and the Alfred P. Sloan Foundation through Grant 2013-10-27 to the University of California. J.M. acknowledges the support of the National Science Foundation under grant DMR-1708615. L.-Q.C. acknowledges the support of the National Science Foundation under grant DMR-1744213. R.Y. and Y.C. acknowledges the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper (URL: http://www.tacc.utexas.edu).
| Funders | Funder number |
|---|---|
| Institute for Functional Materials and Devices | |
| Institute for Intelligent Systems and Computation at Lehigh University | |
| Interfaces | |
| National Science Foundation BIGDATA | |
| Office of Basic Energy Sciences | |
| National Science Foundation | RES-1839234, 1708615, 1251274, 1839234, 1744213 |
| U.S. Department of Energy | |
| Army Research Office | W911NF-14-1-0104 |
| Alfred P. Sloan Foundation | 2013-10-27 |
| Gordon and Betty Moore Foundation | GBMF3834 |
| University of California | DMR-1744213, DMR-1708615 |
| Office of Science | |
| Division of Materials Sciences and Engineering |