We report the development and experimental implementation of the automated experiment workflows for the identification of thebest predictive channel for a phenomenon of interest in spectroscopic measurements. The appr...We report the development and experimental implementation of the automated experiment workflows for the identification of thebest predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combinationof ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. Itallows the identification of which of the available observational channels, sampled sequentially, are most predictive of selectedbehaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse forcemicroscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictivechannel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. Thesame workflow and code are applicable for any multimodal imaging and local characterization methods.展开更多
The original version of this Article did not acknowledge Rama K.Vasudevan(vasudevanrk@ornl.gov)as a corresponding author.This has now been corrected in both the PDF and HTML versions of the Article.
Spatially resolved time and voltage-dependent polarization dynamics in PbTiO3 thin films is explored using dynamic piezoresponse force microscopy(D-PFM)in conjunction with interferometric displacement sensing.This app...Spatially resolved time and voltage-dependent polarization dynamics in PbTiO3 thin films is explored using dynamic piezoresponse force microscopy(D-PFM)in conjunction with interferometric displacement sensing.This approach gives rise to 4D data sets containing information on bias-dependent relaxation dynamics at each spatial location without long-range electrostatic artifacts.To interpret these data sets in the absence of defined physical models,we employ a non-negative tensor factorization method which clearly presents the data as a product of simple behaviors allowing for direct physics interpretation.Correspondingly,we perform phase-field modeling finding the existence of‘hard’and‘soft’domain wall edges.This approach can be extended to other multidimensional spectroscopies for which even exploratory data analysis leads to unsatisfactory results due to many components in the decomposition.展开更多
基金This effort(implementation in SPM,measurement,data analysis)was primarily supported by the center for 3D Ferroelectric Microelectronics(3DFeM),an Energy Frontier Research Center funded by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences under Award Number DE-SC0021118This research(ensemble-DKL)was supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National LaboratoryThis work was also supported by MEXT Program:Data Creation and Utilization Type Material Research and Development Project Grant Number JPMXP1122683430.
文摘We report the development and experimental implementation of the automated experiment workflows for the identification of thebest predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combinationof ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. Itallows the identification of which of the available observational channels, sampled sequentially, are most predictive of selectedbehaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse forcemicroscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictivechannel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. Thesame workflow and code are applicable for any multimodal imaging and local characterization methods.
文摘The original version of this Article did not acknowledge Rama K.Vasudevan(vasudevanrk@ornl.gov)as a corresponding author.This has now been corrected in both the PDF and HTML versions of the Article.
基金This research used resources of the Compute and Data Environment for Science(CADES)at the Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S Department of Energy under Contract No.DE-AC05-00OR22725This work was partially supported by the JSPSKAKENHI Grant Nos.15H04121,and 26220907(H.F.).
文摘Spatially resolved time and voltage-dependent polarization dynamics in PbTiO3 thin films is explored using dynamic piezoresponse force microscopy(D-PFM)in conjunction with interferometric displacement sensing.This approach gives rise to 4D data sets containing information on bias-dependent relaxation dynamics at each spatial location without long-range electrostatic artifacts.To interpret these data sets in the absence of defined physical models,we employ a non-negative tensor factorization method which clearly presents the data as a product of simple behaviors allowing for direct physics interpretation.Correspondingly,we perform phase-field modeling finding the existence of‘hard’and‘soft’domain wall edges.This approach can be extended to other multidimensional spectroscopies for which even exploratory data analysis leads to unsatisfactory results due to many components in the decomposition.
