A tunable high-Q surface acoustic wave(SAW)resonator in the form of several parallel-connected interdigital transducers loaded on a varying capacitance on lithium niobate substrates was developed and studied.The worki...A tunable high-Q surface acoustic wave(SAW)resonator in the form of several parallel-connected interdigital transducers loaded on a varying capacitance on lithium niobate substrates was developed and studied.The working frequency range was 90-2450 MHz.A method of calculating such resonators,considering losses in the metal film as well as losses due to the propagation of SAWs and transformations into bulk waves is proposed.Such a design allows one to obtain a quality factor over 5000 in the frequency range 2400-2483 MHz.The resonant frequency shifts by 600 kHz when the capacitance changes by±25%of the value of 21 pF(or 32 ppm/pF)and has an almost linear character.展开更多
The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and...The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.展开更多
Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and clo...Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and closed-loop microscope operation.The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization.Here,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop operations.Specifically,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple platforms.These considerations will collectively inform the operationalization of ML in next-generation experimentation.展开更多
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 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 surfaces.This plethora of data contain...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 surfaces.This plethora of data contains an immense volume of information on materials structures,structural distortions,and physical functionalities.Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information.However,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 phenomena.Here,we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework.We 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.展开更多
The rapid development of spectral-imaging methods in scanning probe,electron,and optical microscopy in the last decade have given rise for large multidimensional datasets.In many cases,the reduction of hyperspectral d...The rapid development of spectral-imaging methods in scanning probe,electron,and optical microscopy in the last decade have given rise for large multidimensional datasets.In 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 determined.However,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 guesses.Here,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.展开更多
Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy(STEM)at high speed,with the potential for vast volumes of data to be acquir...Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy(STEM)at high speed,with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods.Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way.However,like many other tasks related to object detection and identification in artificial intelligence,it is challenging to detect and identify defects from STEM images.Furthermore,it is difficult to deal with crystal structures that have many atoms and low symmetries.Previous methods used for defect detection and classification were based on supervised learning,which requires human-labeled data.In this work,we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine(OCSVM).We introduce two schemes of image segmentation and data preprocessing,both of which involve taking the Patterson function of each segment as inputs.We demonstrate that this method can be applied to various defects,such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.展开更多
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 extra...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 models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,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.展开更多
The 4D scanning transmission electron microscopy(STEM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fiel...The 4D scanning transmission electron microscopy(STEM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fields,structural and electronic order parameters,and other symmetry breaking distortions.A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors.We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders(rrVAE),which disentangle the general rotation of the object from other latent representations.The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures.The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis.This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.展开更多
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 f...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.展开更多
Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials,naturally offering the challenge of using these...Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials,naturally offering the challenge of using these as starting inputs for atomistic simulations.In this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic configurations.However,this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy,as well as latencies of microscopy and simulations per se.Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment,to enable the selection of regions of interest and exploring them using physical simulations.Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors.The pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow.This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects.However,it is universal and can be used for other material systems.展开更多
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.Howe...Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.However,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different ones.This limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies.Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection.This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble.This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.展开更多
Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferro...Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.展开更多
The coupling of atomic force microscopy with infrared spectroscopy(AFM-IR)offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution.H...The coupling of atomic force microscopy with infrared spectroscopy(AFM-IR)offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution.However,in order to fully utilize the measurement capability of AFM-IR,a three-dimensional dataset(2D map with a spectroscopic dimension)needs to be acquired,which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan.In this paper,we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm.This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image,greatly reducing data acquisition time.As a result,we are able to generate highresolution maps of component distribution,produce chemical maps at any wavenumber available in the spectral range,and perform correlative analysis of the physical and chemical properties of the samples.We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition.We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.展开更多
Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets.The multivariate linear unmixing methods...Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets.The multivariate linear unmixing methods generally explore similarities in the energy dimension,but ignore correlations in the spatial domain.At the same time,Gaussian process(GP)explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive.Here,we implement a GP method operating on the full spatial domain and reduced representations in the energy domain.In 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 morphology.We explore the role of kernel constraints on the quality of the reconstruction,and suggest an approach for estimating them from the experimental data.We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.展开更多
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
Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions.However,such theories that work well for semiconductors tend to fail i...Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions.However,such theories that work well for semiconductors tend to fail in materials with strong correlations,either in electronic behavior or chemical segregation.In these cases,the details of atomic arrangements are generally not explored and analyzed.The 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 derived.To 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 formation.Given 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 testing.We 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.展开更多
Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic r...Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.展开更多
Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real s...Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision.In many technologically relevant atomic and/or molecular systems,however,the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature.One of the critical issues,therefore,lies in being able to accurately identify(‘read out’)all the individual building blocks in different atomic/molecular architectures,as well as more complex patterns that these blocks may form,on a scale of hundreds and thousands of individual atomic/molecular units.Here we employ machine vision to read and recognize complex molecular assemblies on surfaces.Specifically,we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements.We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorderproperty relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale,and elucidate reaction pathway involving molecular conformation changes.The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural,electronic,and magnetic orders in different condensed matter systems.展开更多
基金This research work is supported by the Russian Ministry of Science and Higher Education under the project No.16.5405.2017/8.9。
文摘A tunable high-Q surface acoustic wave(SAW)resonator in the form of several parallel-connected interdigital transducers loaded on a varying capacitance on lithium niobate substrates was developed and studied.The working frequency range was 90-2450 MHz.A method of calculating such resonators,considering losses in the metal film as well as losses due to the propagation of SAWs and transformations into bulk waves is proposed.Such a design allows one to obtain a quality factor over 5000 in the frequency range 2400-2483 MHz.The resonant frequency shifts by 600 kHz when the capacitance changes by±25%of the value of 21 pF(or 32 ppm/pF)and has an almost linear character.
基金K.C.thanks the computational support from XSEDE computational resources under allocation number TGDMR 190095Contributions from K.C.were supported by the financial assistance award 70NANB19H117 from the U.S.Department of Commerce,National Institute of Standards and Technology+3 种基金Contributions by S.M.,K.H.,K.R.,and D.V.were supported by NSF DMREF Grant No.DMR-1629059 and No.DMR-1629346X.Q.was supported by NSF Grant No.OAC-1835690A.A.acknowledges partial support by CHiMaD(NIST award#70NANB19H005)G.P.was supported by the Los Alamos National Laboratory’s Laboratory Directed Research and Development(LDRD)program’s Directed Research(DR)project#20200104DR。
文摘The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.
文摘Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and closed-loop microscope operation.The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization.Here,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop operations.Specifically,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple platforms.These considerations will collectively inform the operationalization of ML in next-generation experimentation.
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.
基金This effort(ML,STEM,film growth,sample growth)is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(S.V.K.,S.V.,G.E.,W.Z.,J.Z.,H.Z.,and R.P.H.)and was performed and partially supported(R.K.V.and M.Z.)at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.Department of Energy,Office of Science User Facility.Dr.Matthew Chisholm is gratefully acknowledged for the STEM data used in this work.Dr.Katharine Page is gratefully acknowledged for help in the data acquisition at NOMAD.A portion of this research used resources at the Spallation Neutron Source,a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory.The authors are deeply grateful to Dr.Karren More for careful reading and correcting the manuscript.
文摘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 surfaces.This plethora of data contains an immense volume of information on materials structures,structural distortions,and physical functionalities.Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information.However,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 phenomena.Here,we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework.We 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.
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-AC05-00OR22725。
文摘The rapid development of spectral-imaging methods in scanning probe,electron,and optical microscopy in the last decade have given rise for large multidimensional datasets.In 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 determined.However,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 guesses.Here,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.
基金This effort is primarily based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(Y.G.,S.V.K.,and A.R.L.).Electron microscopy with Nion UltraSTEM 100 and TEM sample preparation were performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.Department of Energy Office of Science User Facility.S.V.K.and A.V.D.acknowledge support through the Materials Genome Initiative funding allocated to NIST.
文摘Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy(STEM)at high speed,with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods.Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way.However,like many other tasks related to object detection and identification in artificial intelligence,it is challenging to detect and identify defects from STEM images.Furthermore,it is difficult to deal with crystal structures that have many atoms and low symmetries.Previous methods used for defect detection and classification were based on supervised learning,which requires human-labeled data.In this work,we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine(OCSVM).We introduce two schemes of image segmentation and data preprocessing,both of which involve taking the Patterson function of each segment as inputs.We demonstrate that this method can be applied to various defects,such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.
基金The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NISTA.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).
文摘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 models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,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.
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-AC05-00OR22725.
文摘The 4D scanning transmission electron microscopy(STEM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fields,structural and electronic order parameters,and other symmetry breaking distortions.A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors.We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders(rrVAE),which disentangle the general rotation of the object from other latent representations.The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures.The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis.This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.
基金M.A.and D.K.acknowledge support from CNMS user facility,project #CNMS2019-272.
文摘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.
文摘Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials,naturally offering the challenge of using these as starting inputs for atomistic simulations.In this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic configurations.However,this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy,as well as latencies of microscopy and simulations per se.Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment,to enable the selection of regions of interest and exploring them using physical simulations.Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors.The pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow.This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects.However,it is universal and can be used for other material systems.
基金This effort(machine learning)is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities(A.G.,S.V.K.,B.G.S.)was also supported(STEM experiment)by the DOE,Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(O.D.),and was performed and partially supported(M.Z.,B.G.S.)at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a DOE Office of Science User Facility.Dr.Matthew Chisholm(ORNL)is gratefully acknowledged for the STEM data on Ni-LSMO used in this work.
文摘Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.However,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different ones.This limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies.Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection.This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble.This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.
基金This STEM effort is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(S.V.K.,C.T.N.).This ML effort is based upon work supported by the U.S.DOE,Office of Science,Office of Basic Energy Sciences Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities(A.G.).The work was performed and partially supported(M.Z.)at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.DOE,Office of Science User Facility.The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301 and the Center for Spintronic Materials in Advanced Information Technologies(SMART)one of the centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST.The authors gratefully acknowledge Dr.Karren More(CNMS)for careful reading and editing the manuscript.
文摘Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.
基金Algorithm development was part of the AI Initiative,sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory(S.J.,R.K.V),managed by UT-Battelle,LLC,for the U.S.Department of Energy(DOE)The plant sciences portion of this work was supported by the Center for Engineering MechanoBiology(CEMB),an NSF Science and Technology Center,under grant agreement CMMI:15-48571(N.B.and M.F.).
文摘The coupling of atomic force microscopy with infrared spectroscopy(AFM-IR)offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution.However,in order to fully utilize the measurement capability of AFM-IR,a three-dimensional dataset(2D map with a spectroscopic dimension)needs to be acquired,which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan.In this paper,we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm.This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image,greatly reducing data acquisition time.As a result,we are able to generate highresolution maps of component distribution,produce chemical maps at any wavenumber available in the spectral range,and perform correlative analysis of the physical and chemical properties of the samples.We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition.We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.
基金This effort(electron microscopy,Gaussian Process workflow)is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(S.V.K.and A.R.L.)and was performed and partially supported(GPim development by M.Z.and R.K.V.)at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.Department of Energy,Office of Science User Facility.
文摘Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets.The multivariate linear unmixing methods generally explore similarities in the energy dimension,but ignore correlations in the spatial domain.At the same time,Gaussian process(GP)explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive.Here,we implement a GP method operating on the full spatial domain and reduced representations in the energy domain.In 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 morphology.We explore the role of kernel constraints on the quality of the reconstruction,and suggest an approach for estimating them from the experimental data.We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.
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.
文摘Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions.However,such theories that work well for semiconductors tend to fail in materials with strong correlations,either in electronic behavior or chemical segregation.In these cases,the details of atomic arrangements are generally not explored and analyzed.The 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 derived.To 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 formation.Given 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 testing.We 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.
基金The work on microscopy and synthesis was supported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Materials Sciences and Engineering Division(R.K.V,S.V.K,K.W,KX.,D.G.)Research was conducted at the Center for Nanophase Materials Sciences,which is a DOE Office of Science User Facility+1 种基金D,SJ.acknowledge support by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory,managed by UT-Battelle,LLC,for the U.S.Department of EnergyA.M.acknowledges fllowship support from pthe UT/ORNL Bredesen Center for Interdisciplinary Research and Graduate Education.
文摘Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.
基金sponsored by the Division of Materials Sciences and Engineering,Office of Science,Basic Energy Sciences,US Department of Energysupport from the UT/ORNL Bredesen Center for Interdisciplinary Research and Graduate Education.
文摘Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision.In many technologically relevant atomic and/or molecular systems,however,the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature.One of the critical issues,therefore,lies in being able to accurately identify(‘read out’)all the individual building blocks in different atomic/molecular architectures,as well as more complex patterns that these blocks may form,on a scale of hundreds and thousands of individual atomic/molecular units.Here we employ machine vision to read and recognize complex molecular assemblies on surfaces.Specifically,we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements.We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorderproperty relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale,and elucidate reaction pathway involving molecular conformation changes.The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural,electronic,and magnetic orders in different condensed matter systems.
