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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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Off-the-shelf deep learning is not enough,and requires parsimony,Bayesianity,and causality

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npj Computational Materials 2021年第1期7卷 133-138页

作者： Rama K.Vasudevan maxim ziatdinov Lukas Vlcek Sergei.V.Kalinin Center for Nanophase Materials Sciences Oak RidgeTN 37831USA Computational Sciences and Engineering Division Oak RidgeTN 37831USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Present address:Bayer St.LouisMO 63141USA

Deep neural networks(‘deep learning’)have emerged as a technology of choice to tackle problems in speech recognition,computer vision,finance,***,adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal,hypothesis driven nature of modern *** argue that the broad adoption of Bayesian methods incorporating prior knowledge,development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models,and ultimately adoption of causal models,offers a path forward for fundamental and applied research.

关键词： learning enough finance

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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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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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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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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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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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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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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

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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

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