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Learning surface molecular structures via machine vision

npj Computational Materials 2017年第1期3卷 194-202页

作者： Maxim Ziatdinov Artem Maksov sergei v.kalinin Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Bredesen Center for Interdisciplinary Research University of TennesseeKnoxvilleTN 37996USA

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 *** 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 *** 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 *** we employ machine vision to read and recognize complex molecular assemblies on ***,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 *** 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 *** 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.

关键词： allowed classify spatially

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E-beam manipulation of Si atoms on graphene edges with an aberration-corrected scanning transmission electron microscope

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Nano Research 2018年第12期11卷 6217-6226页

作者： Ondrej Dyck Songkil Kim sergei v.kalinin Stephen Jesse Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37830USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37830USA School of Mechanical Engineering Pusan National UniversityBusan 46241Republic of Korea

The burgeoning field of atomic-level material control holds great promise for future breakthroughs in quantum and memristive device manufacture and fundamental studies of atomic-scale chemistry. Realization of atom-by-atom control of matter represents a complex and ongoing challenge. Here, we explore the feasibility of controllable motion of dopant Si atoms at the edges of graphene via the sub-atomically focused electron beam in a scanning transmission electron microscope. We demonstrate that the graphene edges can be cleaned of Si atoms and then subsequently replenished from nearby source material. It is also shown how Si edge atoms may be “pushed” from the edge of a small hole into the bulk of the graphene lattice and from the bulk of the lattice back to the edge. This is accomplished through sputtering of the edge of the graphene lattice to bury or uncover Si dopant atoms. Finally, we demonstrate e-beam mediated hole healing and incorporation of dopant atoms. These experiments form an initial step toward general atomic-scale material control.

关键词： atomic manipulation graphene scanning transmission electron microscopy edge passivation silicon dopant electron beam dynamics

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Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

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npj Computational Materials 2022年第1期8卷 705-715页

作者： Ayana Ghosh Maxim Ziatdinov Ondrej Dyck Bobby G.Sumpter sergei v.kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN37831USA

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 *** this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic ***,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 *** 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 *** 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 *** pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the *** 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 ***,it is universal and can be used for other material systems.

关键词： dynamics bridges offering

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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

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Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

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npj Computational Materials 2021年第1期7卷 909-916页

作者： Ayana Ghosh Bobby G.Sumpter Ondrej Dyck sergei v.kalinin Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

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 ***,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 *** 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 *** 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 *** 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 *** methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

关键词： learning experiment enable

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Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

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npj Computational Materials 2021年第1期7卷 1342-1352页

作者： Christopher T.Nelson Ayana Ghosh Mark Oxley Xiaohang Zhang Maxim Ziatdinov Ichiro Takeuchi sergei v.kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA Department of Materials Science and Engineering University of MarylandCollege ParkMDUSA

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 *** 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 ***,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 *** 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 *** analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

关键词： Deep network ferroelectric

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Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

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npj Computational Materials 2021年第1期7卷 595-600页

作者： Mark P.Oxley Maxim Ziatdinov Ondrej Dyck Andrew R.Lupini Rama vasudevan sergei v.kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

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 *** 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 *** implementation of purely rotational rrvAE is discussed as are applications to simulated data for graphene and zincblende *** rrvAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass *** approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.

关键词： symmetry breaking rotational

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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

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Defect detection in atomic-resolution images via unsupervised learning with translational invariance

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npj Computational Materials 2021年第1期7卷 1640-1648页

作者： Yueming Guo sergei v.kalinin Hui Cai Kai Xiao Sergiy Krylyuk Albert v.Davydov Qianying Guo Andrew R.Lupini Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA

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 *** detection and classification of defects in the STEM images are needed in order to handle the data in an efficient ***,like many other tasks related to object detection and identification in artificial intelligence,it is challenging to detect and identify defects from STEM ***,it is difficult to deal with crystal structures that have many atoms and low *** methods used for defect detection and classification were based on supervised learning,which requires human-labeled *** 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 *** 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.

关键词： handle resolution image

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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

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