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Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy

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npj Computational Materials 2023年第1期9卷 2066-2073页

作者： Panithan Sriboriboon Huimin Qiao Owoong kwon rama k.vasudevan Stephen Jesse Yunseok kim School of Advanced Materials and Engineering Sungkyunkwan University(SKKU)SuwonRepublic of Korea Research Center for Advanced Materials Technology Core Research InstituteSuwonRepublic of Korea Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA

Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity,even in ultra-thin film ***,studying the weak response from ultra-thin film requires improved measurement *** general,resonance-enhanced piezoresponse force microscopy(PFM)has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance ***,an iterative approach,such as traditional least squares(LS)fitting,is sensitive to noise and can result in the misunderstanding of weak *** this study,we developed the deep neural network(DNN)hybrid with deep denoising autoencoder(DDA)and principal component analysis(PCA)to extract resonance *** DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm,allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf_(0.5)Zr_(0.5)O_(2) thin *** hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.

关键词： spectrum ferroelectric excitation

Gaussian process analysis of electron energy loss spectroscopy data:multivariate reconstruction and kernel control

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npj Computational Materials 2021年第1期7卷 1395-1404页

作者： Sergei V.kalinin Andrew R.Lupini rama k.vasudevan Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA

Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data *** multivariate linear unmixing methods generally explore similarities in the energy dimension,but ignore correlations in the spatial *** the same time,Gaussian process(GP)explicitly incorporate spatial correlations in the form of kernel functions but is computationally ***,we implement a GP method operating on the full spatial domain and reduced representations in the energy *** this multivariate GP,the information between the components is shared via a common spatial kernel structure,while allowing for variability in the relative noise magnitude or image *** explore the role of kernel constraints on the quality of the reconstruction,and suggest an approach for estimating them from the experimental *** further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

关键词： domain kernel multivariate

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

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npj Computational Materials 2023年第1期9卷 2008-2015页

作者： Yongtao Liu rama k.vasudevan kyle P.kelley Hiroshi Funakubo Maxim Ziatdinov Sergei V.kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37923USA Department of Materials Science and Engineering Tokyo Institute of TechnologyYokohama 226-8502Japan Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37923USA Department of Materials Science and Engineering University of TennesseeKnoxvilleTN 37996USA

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.

关键词： automated policy modal

Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy

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npj Computational Materials 2019年第1期5卷 932-939页

作者： Nikolay Borodinov Sabine Neumayer Sergei V.kalinin Olga S.Ovchinnikova rama k.vasudevan Stephen Jesse Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA

The rapid development of spectral-imaging methods in scanning probe,electron,and optical microscopy in the last decade have given rise for large multidimensional *** many cases,the reduction of hyperspectral data to the lower-dimension materialsspecific parameters is based on functional fitting,where an approximate form of the fitting function is known,but the parameters of the function need to be ***,functional fits of noisy data realized via iterative methods,such as least-square gradient descent,often yield spurious results and are very sensitive to initial ***,we demonstrate an approach for the reduction of the hyperspectral data using a deep neural network approach.A combined deep neural network/least-square approach is shown to improve the effective signal-to-noise ratio of band-excitation piezoresponse force microscopy by more than an order of magnitude,allowing characterization when very small driving signals are used or when a material’s response is weak.

关键词： neural networks property

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

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npj Computational Materials 2021年第1期7卷 397-405页

作者： Lukas Vlcek Shize Yang Yongji Gong Pulickel Ajayan Wu Zhou Matthew FChisholm Maxim Ziatdinov rama k.vasudevan Sergei V.kalinin Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTNUSA Joint Institute for Computational Sciences University of TennesseeKnoxvilleOak RidgeTNUSA Center for Functional Nanomaterials Brookhaven National LaboratoryBrookhavenNYUSA School of Materials Science and Engineering Beihang UniversityBeijingChina Department of Materials Science and NanoEngineering Rice UniversityHoustonTXUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA

Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid ***,such theories that work well for semiconductors tend to fail in materials with strong correlations,either in electronic behavior or chemical *** these cases,the details of atomic arrangements are generally not explored and *** knowledge of the generative physics and chemistry of the material can obviate this problem,since defect configuration libraries as stochastic representation of atomic level structures can be generated,or parameters of mesoscopic thermodynamic models can be *** obtain such information for improved predictions,we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure *** the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images,we combine model optimization techniques with the principles of statistical hypothesis *** demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo_(x)Re_(1-x)S_(2) at varying ratios of Mo/Re stoichiometries,for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

关键词： atomic resolved dopant

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

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npj Computational Materials 2020年第1期6卷 1477-1483页

作者： Maxim Ziatdinov Dohyung kim Sabine Neumayer rama k.vasudevan Liam Collins Stephen Jesse Mahshid Ahmadi Sergei V.kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Joint Institute for Advanced Materials Department of Materials Science and EngineeringUniversity of TennesseeKnoxvilleTN 37996USA

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)*** for weakly informative priors,GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency *** 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 *** the same time,reliable reconstruction along the frequency domain requires the resonance peak to be within the measured *** behavior suggests the optimal strategy for the BE imaging on unknown ***,we discuss how GP can be used for automated experimentation in SPM,by combining GP regression with non-rectangular scans.

关键词： methods Gaussian weakly

Reconstructing phase diagrams from local measurements via Gaussian processes:mapping the temperature-composition space toconfidence

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npj Computational Materials 2018年第1期4卷 451-461页

作者： Dhiren k.Pradhan Shalini kumari Evgheni Strelcov Dillip k.Pradhan Ram S.katiyar Sergei V.kalinin Nouamane Laanait rama k.vasudevan Department of Physics and Institute of Functional Nanomaterials University of Puerto Rico San Juan PR 00936 USA Institute for Research in Electronics and Applied Physics University of MarylandCollege ParkMD 207425USA Department of Physics&Astronomy National Institute of TechnologyRourkela 769008India Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37831USA Geophysical Laboratory Carnegie Institution for ScienceWashingtonDC 20015USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA

We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition,with the voltage spectroscopy data being used as a proxy for the(unknown)microscopic state or thermodynamic parameters of *** the discrete nature of the measurement points,we use Gaussian processes to reconstruct hysteresis loops in temperature and voltage space,and compare the results with the raw data and bulk dielectric spectroscopy *** results indicate that the surface transition temperature is similar for all but one composition with respect to the *** clustering algorithms,we recreate the main features of the bulk diagram,and provide statistical confidence estimates for the reconstructed phase transition *** validate the method by using Gaussian processes to predict hysteresis loops for a given temperature for a composition unseen by the algorithm,and compare with *** techniques can be used to map phase diagrams from functional materials in an automated fashion,and provide a method for uncertainty quantification and model selection.

关键词： materials hysteresis phase

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

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npj Computational Materials 2020年第1期6卷 570-578页

作者： Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang rama k.vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.kalinin The Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742USA Institute for Problems of Materials Science National Academy of Sciences of UkraineKrjijanovskogo 3Kyiv 03142Ukraine Institute of Physics National Academy of Sciences of Ukraine46pr.NaukyKyiv 03028Ukraine

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural ***,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding ***,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions.

关键词： ferroelectric analysis founding

Deep Bayesian local crystallography

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npj Computational Materials 2021年第1期7卷 1649-1660页

作者： Sergei V.kalinin Mark P.Oxley Mani Valleti Junjie Zhang Raphael P.Hermann Hong Zheng Wenrui Zhang Gyula Eres rama k.vasudevan Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Bredesen Center for Interdisciplinary Research The University of TennesseeKnoxvilleTN 37996USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Materials Science Division Argonne National LaboratoryArgonneIL 60439USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Present address:State key Laboratory of Crystal Materials&Institute of Crystal Materials Shandong UniversityJinanShandong 250100China

The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials,2D materials,and *** plethora of data contains an immense volume of information on materials structures,structural distortions,and physical *** this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant ***,the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics,notably translational and point group symmetries and symmetry lowering ***,we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian *** demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting,namely rotationally invariant variational autoencoders.

关键词： symmetry local resolved