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Determination of the major geochemical processes of groundwater along the Cretaceous-Tertiary boundary of Trichinopoly,Tamilnadu,India

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Acta Geochimica 2020年第5期39卷 760-781页

作者： N.Devaraj S.Chidambaram u.vasudevan K.Pradeep M.Nepolian M.V.Prasanna V.S.Adithya R.Thilagavathi C.Thivya Banajarani Panda Department of Earth Sciences Annamalai UniversityChidambaramTamilnadu 608002India Water Research Centre Kuwait Institute for Scientific ResearchKuwait CityKuwait Department of Applied Geology Faculty of Engineering and ScienceCurtin University Malaysia.CDT 25098009 MiriSarawakMalaysia

The hydrogeochemical variations in groundwater are mainly influenced by lithology,residence time of water in the aquifer matrix,and anthropogenic *** study was focused on the geochemical variations of groundwater in different lithological units(Archaean,Cretaceous,Tertiary,and Quaternary)by understanding the major factors governing the geochemical variations in each *** 71 groundwater samples were collected from these rock types,namely,Archaean(14),Cretaceous(37),Tertiary(11),Quaternary(9).The collected samples were measured for major ions and they were used for preparation of standard geochemical plots and ionic *** analysis and factor score were used to identify the major factors controlling the hydrochemistry and their spatial distribution in the study *** addition,geochemical model,WATEQ 4 F was used to determine the saturation condition of carbonate and sulphate minerals in the ***–Cl and mixed Ca–Na–HCO3 were the dominant hydrochemical facies irrespective of lithological *** overall interpretation of geochemical data revealed that leaching of secondary salts,weathering and ion exchange reaction along the groundwater flow path through various lithological units,and anthropogenic influence from domestic sewages and agricultural activities constitute the major geochemical processes in the study ***,this study brings out the multiple hydrogeochemical process in the complex geological terrain along the Cretaceous-Tertiary boundary.

关键词： Groundwater Geochemical process Major ions Correlation Factor analysis

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

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

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

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

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

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR

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npj Computational Materials 2019年第1期5卷 714-722页

作者： Nikolay Borodinov Natasha Bilkey Marcus Foston Anton V.Ievlev Alex Belianinov Stephen Jesse Rama K.vasudevan Sergei V.Kalinin Olga S.Ovchinnikova Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Energy Environmental&Chemical EngineeringWashington University in St.LouisOne Brookings DriveSt.LouisMO 63130USA

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 ***,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 *** this paper,we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening *** 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 *** 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 *** 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 *** 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.

关键词： utilize modal walls