Abstract
A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of x-ray diffraction, photoluminescence, Raman spectra, and other data sets.
| Original language | English |
|---|---|
| Article number | 045028 |
| Journal | Machine Learning: Science and Technology |
| Volume | 2 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2021 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This effort (ML and PFM) is based upon work supported by the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0021118 (Y L, K P K, S V K), and the Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility (M Z, R K V). D K and M A acknowledge support from CNMS user facility, project number CNMS2019-272. Y S acknowledges the support from the G T Seaborg Fellowship (project number 20210527CR) and the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy Office of Science at Los Alamos National Laboratory. The authors are thankful to Professor Hiroshi Funakubo (Tokyo Institute of Technology) for providing PTO samples. This effort (ML and PFM) is based upon work supported by the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0021118 (Y L, K P K, S V K), and the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility (M Z, R K V). D K and M A acknowledge support from CNMS user facility, project number CNMS2019-272. Y S acknowledges the support from the G T Seaborg Fellowship (project number 20210527CR) and the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy Office of Science at Los Alamos National Laboratory. The authors are thankful to Professor Hiroshi Funakubo (Tokyo Institute of Technology) for providing PTO samples.
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| G T Seaborg Fellowship | 20210527CR |
| Oak Ridge National Laboratory | |
| Oak Ridge National Laboratory | CNMS2019-272 |
| United States Government | |
| center for 3D Ferroelectric Microelectronics | |
| U.S. Department of Energy | |
| Office of Science | |
| Basic Energy Sciences | DE-SC0021118 |
| Los Alamos National Laboratory | |
| Center for Integrated Nanotechnologies | |
| Tokyo Institute of Technology |
Keywords
- Band excitation piezoresponse force microscopy
- Invariant variational autoencoder
- Scanning probe microscopy