Abstract
Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.
| Original language | English |
|---|---|
| Article number | 1800701 |
| Journal | Advanced Materials |
| Volume | 30 |
| Issue number | 28 |
| DOIs | |
| State | Published - Jul 12 2018 |
Funding
Y.C. and B.N. contributed equally to this work. J.C.A. acknowledges partial support from the Army Research Office under grant W911NF-14-1-0104 and partial 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. S.P. acknowledges support from the National Science Foundation under grant DMR-1708615. A.I.L. acknowledges support from the National Science Foundation under grant OISE-1545907. J.T.M. acknowledges support from the National Science Foundation under grant DMR-1451219. L.W.M. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract number DE-SC-0012375 for development of the ferroelectric thin films. The BEPS portion of this research was conducted at the Center for Nanophase Materials Sciences, which also provided support (S.V.K., R.K.V., S.J., N.B.) and is a US DOE Office of Science User Facility. The phase field simulations portion of this research was supported by the U.S. Department of Energy, Office of Science, Materials Sciences and Engineering Division (Y.C.). A portion of this research (B.N., S.v.d.W.) was sponsored by the Gordon and Betty Moore Foundation Data-Driven Discovery and (B.N.) NSF BIGDATA Grant no. 1251274.
| Funders | Funder number |
|---|---|
| DOE Office of Science | |
| Office of Basic Energy Sciences | |
| National Science Foundation | DE-SC-0012375, DMR-1451219, DMR-1708615, OISE-1545907 |
| U.S. Department of Energy | |
| Directorate for Computer and Information Science and Engineering | 1251274 |
| Army Research Office | W911NF-14-1-0104 |
| Gordon and Betty Moore Foundation | |
| Office of Science | |
| Division of Materials Sciences and Engineering | DE-AC02-05-CH11231 |
Keywords
- PZT
- domain structures
- ferroelectric materials
- machine learning
- scanning-probe microscopy