We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.
Ziatdinov, Maxim, et al. "Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling." npj Computational Materials, vol. 6, no. 1, Mar. 2020. https://doi.org/10.1038/s41524-020-0289-6
Ziatdinov, Maxim, Kim, Dohyung, Neumayer, Sabine M., Vasudevan, Rama K., Collins, Liam, Jesse, Stephen, Ahmadi, Mahshid, & Kalinin, Sergei V. (2020). Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling. npj Computational Materials, 6(1). https://doi.org/10.1038/s41524-020-0289-6
Ziatdinov, Maxim, Kim, Dohyung, Neumayer, Sabine M., et al., "Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling," npj Computational Materials 6, no. 1 (2020), https://doi.org/10.1038/s41524-020-0289-6
@article{osti_1608212,
author = {Ziatdinov, Maxim and Kim, Dohyung and Neumayer, Sabine M. and Vasudevan, Rama K. and Collins, Liam and Jesse, Stephen and Ahmadi, Mahshid and Kalinin, Sergei V.},
title = {Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling},
annote = {We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.},
doi = {10.1038/s41524-020-0289-6},
url = {https://www.osti.gov/biblio/1608212},
journal = {npj Computational Materials},
issn = {ISSN 2057-3960},
number = {1},
volume = {6},
place = {United States},
publisher = {Nature Publishing Group},
year = {2020},
month = {03}}