arXiv subjects

Sergei V. Kalinin

Publications and source records attributed to Sergei V. Kalinin.

At least 181 records · Page 10Linked to original sources

arXiv2020

Predictability of localized plasmonic responses in nanoparticle assemblies

Design of nanoscale structures with desired nanophotonic properties are key tasks for nanooptics and nanophotonics. Here, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the correlative relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of the local responses based on geometries for fixed compositions and chemical states of the surface. The analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that can yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures.

cond-mat.mtrl-sci↗

arXiv2020

Separating physically distinct mechanisms in complex infrared plasmonic nanostructures via machine learning enhanced electron energy loss spectroscopy

Low-loss electron energy loss spectroscopy (EELS) has emerged as a technique of choice for exploring the localization of plasmonic phenomena at the nanometer level, necessitating analysis of physical behaviors from 3D spectral data sets. For systems with high localization, linear unmixing methods provide an excellent basis for exploratory analysis, while in more complex systems large numbers of components are needed to accurately capture the true plasmonic response and the physical interpretability of the components becomes uncertain. Here, we explore machine learning based analysis of low-loss EELS data on heterogeneous self-assembled monolayer films of doped-semiconductor nanoparticles, which support infrared resonances. We propose a pathway for supervised analysis of EELS datasets that separate and classify regions of the films with physically distinct spectral responses. The classifications are shown to be robust, to accurately capture the common spatiospectral tropes of the complex nanostructures, and to be transferable between different datasets to allow high-throughput analysis of large areas of the sample. As such, it can be used as a basis for automated experiment workflows based on Bayesian optimization, as demonstrated on the ex situ data. We further demonstrate the use of non-linear autoencoders (AE) combined with clustering in the latent space of the AE yields highly reduced representations of the system response that yield insight into the relevant physics that do not depend on operator input and bias. The combination of these supervised and unsupervised tools provides complementary insight into the nanoscale plasmonic phenomena.

cond-mat.mtrl-sci↗

arXiv2020

Phenomenological description of bright domain walls in ferroelectric-antiferroelectric layered chalcogenides

Recently, a layered ferroelectric CuInP2Se6 was shown to exhibit domain walls with locally enhanced piezoresponse - a striking departure from the observations of nominally zero piezoresponse in most ferroelectrics. Although it was proposed that such "bright" domain walls are phase-boundaries between ferri- and antiferroelectrically ordered regions of the materials, the physical mechanisms behind the existence and response of these boundaries remain to be understood. Here, using Landau-Ginzburg-Devonshire phenomenology combined with four sub-lattices model, we describe quantitatively the bright-contrast and dark-contrast domain boundaries between the antiferroelectric, ferroelectric or ferrielectric long-range ordered phases in a layered ferroelectric-antiferroelectric ferroics, such as CuInP2(S1-ySey)6

cond-mat.mtrl-sci↗

arXiv2020

Mesoscopic structure of mixed type domain walls in multiaxial ferroelectrics

The structure of 180-degree uncharged rotational domain wall in a multiaxial ferroelectric film is studied within the framework of analytical Landau-Ginzburg-Devonshire (LGD) approach. The Finite Element Modelling (FEM) is used to solve numerically the system of the coupled nonlinear Euler-Lagrange (EL) differential equations of the second order for two components of polarization. We show that the structure of the domain wall and corresponding (meta)stable phase of the film are controlled by a single master parameter, dimensionless ferroelectric anisotropy μ. We fitted the static profile of a solitary domain wall, calculated by FEM, with hyperbolic functions for polarization components, and extracted the five μ-dependent parameters from the fitting to FEM curves. The high accuracy of the fitting results allows us to conclude that the analytical functions can be treated as the high-accuracy variational solution of the static EL equations with cubic nonlinearity. We further derive the two-component analytical solutions of the static EL equations for a polydomain 180-degree domain structure in a multiaxial ferroelectric film. The analysis of the free energy dependence on the film thickness and boundary conditions at its surfaces allows to select the domain states corresponding to the minimal energy. The single-domain state is ground for zero polarization derivative at the surfaces, while the poly-domain states minimize the system energy for zero polarization at the surfaces. Counterintuitively, the energy of the polydomain states split into two levels 0 and 1 for zero polarization at the surfaces, and each of the levels is the infinite set of the close-energy sub-levels, which morphology is characterized by different structure of the two-component polarization nodes.

cond-mat.mtrl-sci↗

arXiv2020

Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics

Advances in predictive modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress-strain curves, ferroelectric hysteresis loops, etc. Here we suggest and implement a Gaussian Process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. We discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. This approach is illustrated via the hysteresis loop engineering in ferroelectric materials, but can be adapted to other functionalities and generative models. The code is open-sourced and available at [github.com/ramav87/Ferrosim].

cond-mat.dis-nn↗

arXiv2020

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods. Gaussian Processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor matrix factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional data sets, that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook (http://bit.ly/39kMtuR) that goes through all the analysis discussed in the paper.

physics.app-ph↗

arXiv2020

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx

cond-mat.mtrl-sci↗

arXiv2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase-separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds can remain undiscovered. Here, we demonstrate that a combination of exploration and exploration-exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians, and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore parameter space of more complex off-lattice and dynamic models.

cond-mat.mtrl-sci↗

arXiv2020

Investigating phase transitions from local crystallographic analysis based on machine learning of atomic environments

Traditionally, phase transitions are explored using a combination of macroscopic functional characterization and scattering techniques, providing insight into average properties and symmetries of the lattice but local atomic level mechanisms during phase transitions generally remain unknown. Here we explore the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalogenides in the MoS2 ReS2 system from the observations of local degrees of freedom, namely atomic positions by Scanning Transmission Electron Microscopy (STEM). We employ local crystallographic analysis based on machine learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. In particular, we argue that the dependence of the average symmetry breaking distortion amplitude on global and local concentration can be used to separate local chemical and global electronic effects on transition. This approach allows exploring atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of realized and observed local stoichiometries and atomic configurations.

cond-mat.mtrl-sci↗

arXiv2020

Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data

Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D Fast Fourier Transforms (FFT), correlation and pair distribution function are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics on the particle-by-particle level. Beyond the macroscopic descriptors, we utilize the knowledge of local particle geometries and configurations to explore the evolution of local geometries and reconstruct the interaction potential between the particles. Finally, we use the machine learning-based feature extraction to define particle neighborhood free of physics constraints. This approach allowed separating the possible classes of particle behavior, identify the associated transition probabilities, and further extend this analysis to identify slow modes and associated configurations, allowing for systematic exploration and predictive modeling of the time dynamics of the system. Overall, this work establishes the DL based workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

physics.comp-ph↗

arXiv2020

Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control

Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of exploratory and subsequently physics-based analysis of multidimensional data sets. The (by now common) multivariate unsupervised linear unmixing methods and their nonlinear analogs generally explore similarities in the energy dimension but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) methods that explicitly incorporate spatial correlations in the form of kernel functions tend to be extremely computationally intensive, while the use of inducing point-based sparse methods often leads to reconstruction artefacts. Here, we suggest and implement a parallel GP method operating on the full spatial domain and reduced representations in the energy domain. In this parallel 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 common spatial structures and kernel constraints on the quality of the reconstruction and suggest an approach for estimating these factors from the experimental data. Application of this method to an example EELS dataset demonstrates that spatial information contained in higher-order components can be reconstructed and spatially localized. This approach can be further applied to other hyperspectral and multimodal imaging modes. The notebooks developed in this manuscript are freely available as part of a GPim package (https://github.com/ziatdinovmax/GPim).

physics.comp-ph↗

arXiv2020

Flexo-induced ferroelectricity in low dimensional transition metal dichalcogenides

We developed a Landau type theory for the description of polar phenomena in low-dimensional transition metal dichalcogenides (TMDs), specifically exploring flexoelectric origin of the polarization induced by a spontaneous bending and by inversion symmetry breaking due to the interactions with substrate. We consider the appearance of the spontaneous out-of-plane polarization due to the flexoelectric coupling with the strain gradient of the spontaneous surface rippling and surface-induced piezoelectricity. Performed calculations proved that the out-of-plane spontaneous polarization, originated from flexoelectric effect in a rippled TMD, is bistable and reversible by a non-uniform electric field. In contrast, the spontaneous polarization induced by a misfit strain and symmetry-sensitive surface-induced piezoelectric coupling, cannot be reversed by an external electric field. The special attention is paid to the spectral analysis of the linear dielectric susceptibility and gain factor, which enhancement is critically important for the observation of the polar phenomena in low-dimensional TMDs by the surface-enhanced vibrational spectroscopy.

cond-mat.mtrl-sci↗

arXiv2020

Off-the-shelf deep learning is not enough: parsimony, Bayes and causality

Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of "AI." Buoyed by these successes, researchers in the physical sciences have made steady progress in incorporating deep learning into their respective domains. However, such adoption brings substantial challenges that need to be recognized and confronted. Here, we discuss both opportunities and roadblocks to implementation of deep learning within materials science, focusing on the relationship between correlative nature of machine learning and causal hypothesis driven nature of physical sciences. We argue that deep learning and AI are now well positioned to revolutionize fields where causal links are known, as is the case for applications in theory. When confounding factors are frozen or change only weakly, this leaves open the pathway for effective deep learning solutions in experimental domains. Similarly, these methods offer a pathway towards understanding the physics of real-world systems, either via deriving reduced representations, deducing algorithmic complexity, or recovering generative physical models. However, extending deep learning and "AI" for models with unclear causal relationship can produce misleading and potentially incorrect results. Here, we argue the broad adoption of Bayesian methods incorporating prior knowledge, development of DL solutions with incorporated physical constraints, and ultimately adoption of causal models, offers a path forward for fundamental and applied research. Most notably, while these advances can change the way science is carried out in ways we cannot imagine, machine learning is not going to substitute science any time soon.

physics.comp-ph↗

arXiv2020

Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, we demonstrate a factor of 5.8 improvement in imaging rate using a combination of sparse spiral scanning with compressive sensing and Gaussian processing reconstruction. It is found that even extremely sparse scans offer strong reconstructions with less than 6 % error for Gaussian processing reconstructions. Further, we analyze the error associated with each reconstructive technique per reconstruction iteration finding the error is similar past approximately 15 iterations, while at initial iterations Gaussian processing outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

cond-mat.mtrl-sci↗

arXiv2020

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution Scanning Transmission Electron Microscopy data

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 ferroelectric perovskite across the ferroelectric-antiferroelectric phase transitions. The combinatorial library of the Sm-doped BiFeO3 is grown to cover the composition range from pure ferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM images are acquired for selected compositions and are used to create a set of local compositional, structural, and polarization field descriptors. The information-geometric causal inference (IGCI) and additive noise model (ANM) analysis are used to establish the pairwise causal directions between the descriptors, ordering the data set in the causal direction. The causal chain for IGCI and ANM across the composition is compared and suggests the presence of common causal mechanisms across the composition series. Ultimately, we believe that the causal analysis of the multimodal data will allow exploring the causal links between multiple competing mechanisms that control the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors.

cond-mat.mtrl-sci↗

arXiv2020

Reconstruction of effective potential from statistical analysis of dynamic trajectories

The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here we develop a method for stochastic reconstruction of effective acting potentials from observed trajectories. Using the Silicon vacancy defect in graphene as a model, we develop a statistical framework to reconstruct the free energy landscape from calculated atomic displacements.

cond-mat.mes-hall↗

arXiv2020

Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening

Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

cond-mat.mes-hall↗

arXiv2020

Bayesian inference in band excitation Scanning Probe Microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e. conversion from detected signal to descriptors specific to tip-surface interactions and subsequently to materials properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation (BE) SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We note that in application of Bayesian methods, special care should be made for proper setting on the problem as model selection vs. establishing practical parameter equivalence. We further explore the non-linear mechanical behaviors at topological defects in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of Duffing resonance frequency and the nonlinearity of the sample surface, suggesting the presence of the hidden elements of domain structure. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls can be significantly broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

physics.comp-ph↗