arXiv subjects

Sergei V. Kalinin

Publications and source records attributed to Sergei V. Kalinin.

At least 37 records · Page 2Linked to original sources

arXiv2026

Dynamic Multiband Microscopy: A Universal Paradigm for Quantitative Nanoscale Metrology

Scanning Probe Microscopy (SPM) is the primary tool for exploring nanoscale functionality, yet standard single-frequency operation is fundamentally limited, because the dynamic tip-sample interaction is mathematically underdetermined. While advanced methods such as Dual Amplitude Resonance Tracking (DART) and Band Excitation (BE) address this by tracking resonance, they face critical limitations: DART suffers from feedback instability on complex topographies, while Band Excitation is constrained by severe trade-offs between spectral resolution and acquisition speed. Here, we introduce Dynamic Multiband Microscopy (DMM), a general framework that bridges these gaps by combining multifrequency excitation with continuous frequency sweeping. We implement this within an automated experimental workflow that autonomously identifies and targets measurement points of interest. In combination with quantitative interferometric detection, this approach brings SPM to the fundamental limits of noise and spectral sensitivity. Validated on ferroelectric nanofibers, this platform enables simultaneous, crosstalk-free 3D polarization mapping, establishing a universal framework for autonomous, high-fidelity nanoscale metrology.

cond-mat.mtrl-sci↗

arXiv2026

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs

Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of characterization and by the difficulty of extracting actionable structure-property relations from complex characterization data. Here we develop an autonomous scanning probe microscopy (SPM) framework that integrates automated atomic force and magnetic force microscopy (AFM/MFM) to rapidly explore magnetic and structural properties across combinatorial spread libraries. To enable automated exploration of systems without a clear optimization target, we introduce a combination of a static physics-informed bag-of-features (BoF) representation of measured surface morphology and magnetic structure with multi-objective Bayesian optimization (MOBO) to discover the relative significance and robustness of features. The resulting closed-loop workflow selectively samples the compositional gradient and reconstructs feature landscapes consistent with dense grid "ground truth" measurements. The resulting Pareto structure reveals where multiple nanoscale objectives are simultaneously optimized, where trade-offs between roughness, coherence, and magnetic contrast are unavoidable, and how families of compositions cluster into distinct functional regimes, thereby turning multi-feature imaging data into interpretable maps of competing structure-property trends. While demonstrated for Au-Co-Ni and AFM/MFM, the approach is general and can be extended to other combinatorial systems, imaging modalities, and feature sets, illustrating how feature-based MOBO and autonomous SPM can transform microscopy images from static data products into active feedback for real-time, multi-objective materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Sizes of Ferroelectricity Appearance and Disappearence in Nanosized Hafnia-Zirconia:Landau-type Theory

Nanosized hafnia-zirconia HfxZr1-xO2 in the form of thin films, multilayers and heterostructures are indispensable silicon-compatible ferroelectric materials for advanced electronic memories and logic devices. The distinctive feature of nanoscale hafnia-zirconia are the critical sizes of ferroelectricity appearance, whereas the critical sizes of ferroelectricity disappearance exist in other ferroelectrics. Using the Landau-Ginzburg-Devonshire free energy functional with higher powers, trilinear and biquadratic couplings of polar, nonpolar and antipolar order parameters, we calculated analytically the strain-dependent critical sizes of the ferroelectricity appearance and disappearance, analyzed how the size effect and mismatch strains influence the phase diagrams and polarization switching barrier in epitaxial HfO2 thin films and nano-islands with the out-of-plane spontaneous polarization. We have shown that the critical thickness/height of out-of-plane spontaneous polarization disappearance is determined by the size dependence of the depolarization field and correlation effects. The critical thickness/height of the ferroelectricity appearance is determined by the size dependence of the effective mismatch strain considering possible appearance of misfit dislocations and lateral relaxion of strains. Derived analytical expressions can be generalized for HfxZr1-xO2 solid solutions, providing that corresponding parameters of the free energy are known from the first principles calculations.

cond-mat.mtrl-sci↗

arXiv2025

Rewards-based image analysis in microscopy

Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.

eess.IV↗

arXiv2025

Bayesian Co-Navigation of a Computational Physical Model and AFM Experiment to Autonomously Survey a Combinatorial Materials Library

Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the first fully automated experimental realization of Bayesian co-navigation - a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical model. Using an automated AFM platform coupled to a kinetic Monte Carlo (kMC) model of thin-film growth, the system infers a set of effective bond energies for the (CrTaWV)x-Mo(1-x) pseudo-binary combinatorial library, progressively adjusting the kMC parameters to decrease the epistemic disparity between simulation and experiment. This real-time theoretical refinement enables the kMC model to capture the behavior of the specific materials system and reveals the mechanistic role of hetero-bonding in governing surface diffusion. Together, these results establish co-navigation as a general strategy for tightly integrating physical models with autonomous experimental platforms to produce interpretable and continually self-correcting theoretical modelling of complex materials systems.

cond-mat.mtrl-sci↗

arXiv2025

Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms

Exploration of complex high-dimensional spaces presents significant challenges in fields such as molecular discovery, process optimization, and supply chain management. Genetic Algorithms (GAs), while offering significant power for creating new candidate spaces, often entail high computational demands due to the need for evaluation of each new proposed solution. On the other hand, Deep Kernel Learning (DKL) efficiently navigates the spaces of preselected candidate structures but lacks generative capabilities. This study introduces an approach that amalgamates the generative power of GAs to create new candidates with the efficiency of DKL-based surrogate models to rapidly ascertain the behavior of new candidate spaces. This DKL-GA framework can be further used to build Bayesian Optimization (BO) workflows. We demonstrate the effectiveness of this approach through the optimization of the FerroSIM model, showcasing its broad applicability to diverse challenges, including molecular discovery and battery charging optimization.

cs.LG↗

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2025

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams. While synthesis of such libraries has advanced since the 1960s and been accelerated by laboratory automation, their broader utility depends on rapid, quantitative measurements of composition-dependent structures and functionalities. Scanning probe microscopies (SPM), including piezoresponse force microscopy (PFM), offer unique potential for providing these functionally relevant, spatially resolved readouts. Here, we demonstrate a fully automated SPM framework for exploring ferroelectric properties across combinatorial libraries, focusing on binary Sm-doped BiFeO3 (SmBFO) and ternary Al$_{1-x-y}$Sc$_x$B$_y$N (Al,Sc,B)N systems. In SmBFO, automated exploration identifies the known morphotropic phase boundary with enhanced ferroelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior emerges at the phase-stability boundary, correlating with variations in morphology and defect concentration. By integrating automated SPM with wavelength-dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition-morphology-defect-property relationships underlying ferroelectric response and demonstrate a pathway toward a multi-tool, high-throughput characterization platform. Finally, we implement Gaussian-process-based single- and multi-objective Bayesian optimization to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing physical rewards and accelerating data-driven physics discovery.

cond-mat.mtrl-sci↗

arXiv2025

Tip-Based Proximity Ferroelectric Switching and Piezoelectric Response in Wurtzite Multilayers

Proximity ferroelectricity is a novel paradigm for inducing ferroelectricity, where a non-ferroelectric polar material, which is unswitchable with an external field below the dielectric breakdown field, becomes a practically switchable ferroelectric in direct contact with a thin switchable ferroelectric layer. Here, we develop a Landau-Ginzburg-Devonshire approach to study the proximity effect of local piezoelectric response and polarization reversal in wurtzite ferroelectric multilayers under a sharp electrically biased tip. Using finite element modeling we analyze the probe-induced nucleation of nanodomains, the features of local polarization hysteresis loops and coercive fields in the Al1-xScxN/AlN bilayers and three-layers. Similar to the wurtzite multilayers sandwiched between two parallel electrodes, the regimes of "proximity switching" (where the multilayers collectively switch) and the regime of "proximity suppression" (where they collectively do not switch) are the only two possible regimes in the probe-electrode geometry. However, the parameters and asymmetry of the local piezo-response and polarization hysteresis loops depend significantly on the sequence of the layers with respect to the probe. The physical mechanism of the proximity ferroelectricity in the local probe geometry is a depolarizing electric field determined by the polarization of the layers and their relative thickness. The field, whose direction is opposite to the polarization vector in the layer(s) with the larger spontaneous polarization (such as AlN), renormalizes the double-well ferroelectric potential to lower the steepness of the switching barrier in the "otherwise unswitchable" polar layers. Tip-based control of domains in otherwise non-ferroelectric layers using proximity ferroelectricity can provide nanoscale control of domain reversal in memory, actuation, sensing and optical applications.

cond-mat.mtrl-sci↗

arXiv2025

Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders

Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying physical structures, such as elementary building blocks in molecular systems or order parameters and phases in crystalline materials. Variational Autoencoders (VAEs) provide a powerful means of constructing such low-dimensional representations, but their performance heavily depends on multiple non-myopic design choices, which are often optimized through trial-and-error and empirical analysis. To enable automated and unbiased optimization of VAE workflows, we investigated reward-based strategies for evaluating latent space representations. Using Piezoresponse Force Microscopy data as a model system, we examined multiple policies and reward functions that can serve as a foundation for automated optimization. Our analysis shows that approximating the latent space with Gaussian Mixture Models (GMM) and Bayesian Gaussian Mixture Models (BGMM) provides a strong basis for constructing reward functions capable of estimating model efficiency and guiding the search for optimal parsimonious representations.

cs.LG↗

arXiv2025

Proximity Ferroelectricity in Compositionally Graded Structures

Proximity ferroelectricity is a novel paradigm for inducing ferroelectricity in a non-ferroelectric polar material such as AlN or ZnO that are typically unswitchable with an external field below their dielectric breakdown field. When placed in direct contact with a thin switchable ferroelectric layer (such as Al1-xScxN or Zn1-xMgxO), they become a practically switchable ferroelectric. Using the thermodynamic Landau-Ginzburg-Devonshire theory, in this work we performed the finite element modeling of the polarization switching in the compositionally graded AlN-Al1-xScxN, ZnO-Zn1-xMgxO and MgO-Zn1-xMgxO structures sandwiched in both a parallel-plate capacitor geometry as well as in a sharp probe-planar electrode geometry. We reveal that the compositionally graded structure allows the simultaneous switching of spontaneous polarization in the whole system by a coercive field significantly lower than the electric breakdown field of unswitchable polar materials. The physical mechanism is the depolarization electric field determined by the gradient of chemical composition "x". The field lowers the steepness of the switching barrier in the otherwise unswitchable parts of the compositionally graded AlN-Al1-xScxN and ZnO-Zn1-xMgxO structures, while it induces a shallow double-well free energy potential in the MgO-like regions of compositionally graded MgO-Zn1-xMgxO structure. Proximity ferroelectric switching of the compositionally graded structures placed in the probe-electrode geometry occurs due to nanodomain formation under the tip. We predict that a gradient of chemical composition "x" significantly lowers effective coercive fields of the compositionally graded AlN-Al1-xScxN and ZnO-Zn1-xMgxO structures compared to the coercive fields of the corresponding multilayers with a uniform chemical composition in each layer.

cond-mat.mtrl-sci↗

arXiv2025

Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties

Anisotropic metal nanostructures exhibit polarization-dependent light scattering. This property has been widely exploited to determine geometries of subwavelength structures using far-field microscopy. Here, we explore the use of variational autoencoders (VAEs) to determine the geometries of gold nanorods (NRs) such as in-plane orientation and aspect ratio under linearly polarized dark-field illumination in an optical microscope. We input polarized dark-field scattering spectra and electron microscopy images into a dual-branch multimodal VAE with a single shared latent space trained on paired spectra-image data, using a learnable linear adapter. We achieve prediction of Au NRs using only polarized dark-field scattering spectra input. We determine geometrical parameters of orientational angle and aspect ratio quantitatively via both dual-VAE and physics-based analysis. We show that orientational angle prediction by dual-VAE performs well with only a small (300 particle) training set, yielding a mean absolute error (MAE) of 14.4 and a concordance correlation coefficient (CCC) of 0.95. This performance is only marginally worse than the physics-based cos(2theta) fitting approach between the scattering intensity and the polarizing angle, which achieves MAE of 8.78 and CCC of 0.99. Aspect ratio determination is also similar for the dual-VAE and physics-based fitting comparison (MAE of 0.21 vs. 0.23 and CCC of 0.53 vs. 0.68). By learning a shared latent manifold linking spectra and morphology, the model can generate NR images with accurate orientation and aspect ratio with spectra-only input in the small-data regime (300 particles), suggesting a general recipe for inverse nano-optical problems requiring both structure and orientation information.

physics.optics↗

arXiv2025

SAM$^{*}$: Task-Adaptive SAM with Physics-Guided Rewards

Image segmentation is a critical task in microscopy, essential for accurately analyzing and interpreting complex visual data. This task can be performed using custom models trained on domain-specific datasets, transfer learning from pre-trained models, or foundational models that offer broad applicability. However, foundational models often present a considerable number of non-transparent tuning parameters that require extensive manual optimization, limiting their usability for real-time streaming data analysis. Here, we introduce a reward function-based optimization to fine-tune foundational models and illustrate this approach for SAM (Segment Anything Model) framework by Meta. The reward functions can be constructed to represent the physics of the imaged system, including particle size distributions, geometries, and other criteria. By integrating a reward-driven optimization framework, we enhance SAM's adaptability and performance, leading to an optimized variant, SAM$^{*}$, that better aligns with the requirements of diverse segmentation tasks and particularly allows for real-time streaming data segmentation. We demonstrate the effectiveness of this approach in microscopy imaging, where precise segmentation is crucial for analyzing cellular structures, material interfaces, and nanoscale features.

cs.CV↗

arXiv2025

Attention-Based Explainability for Structure-Property Relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterization. However, this necessitates development of methods for physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure-property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations (SHAP) analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

cond-mat.mtrl-sci↗

arXiv2025

Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings

As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular properties. This paper presents an active learning approach for molecular discovery using Deep Kernel Learning (DKL), demonstrated on the QM9 dataset. DKL links structural embeddings directly to properties, creating organized latent spaces that prioritize relevant property information. By iteratively recalculating embedding vectors in alignment with target properties, DKL uncovers concentrated maxima representing key molecular properties and reveals unexplored regions with potential for innovation. This approach underscores DKL's potential in advancing molecular research and discovery.

cs.LG↗

arXiv2025

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci↗

arXiv2025

Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy

Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features renders systematic exploration by manual or grid-based spectroscopic measurements impractical. Here, we introduce a multi-objective kernel-learning workflow that infers the microstructural rules governing switching behavior directly from high-resolution imaging data. Applied to automated piezoresponse force microscopy (PFM) experiments, our framework efficiently identifies the key relationships between domain-wall configurations and local switching kinetics, revealing how specific wall geometries and defect distributions modulate polarization reversal. Post-experiment analysis projects abstract reward functions, such as switching ease and domain symmetry, onto physically interpretable descriptors including domain configuration and proximity to boundaries. This enables not only high-throughput active learning, but also mechanistic insight into the microstructural control of switching phenomena. While demonstrated for ferroelectric domain switching, our approach provides a powerful, generalizable tool for navigating complex, non-differentiable design spaces, from structure-property correlations in molecular discovery to combinatorial optimization across diverse imaging modalities.

cond-mat.mtrl-sci↗

arXiv2025

Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM

Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of the wall-pinning using scanning-probe microscopy techniques. The pinning centers and preexisting domain walls are usually sparse within image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large area epitaxial PbTiO$_3$ film on cubic KTaO$_3$ were investigated to quantify the electric field driven dynamics of the polar-strain domain structures using ML-controlled automated Piezoresponse Force Microscopy. Analysis of 1500 switching events reveals that domain wall displacement depends not only on field parameters but also on the local ferroelectric-ferroelastic configuration. For example, twin boundaries in polydomains regions like a$_1^-$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20V to 30V, whereas single variant boundaries like a$_2^+$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stack are already activated at 20V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated, high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. This microstructure-specific rule set forms the foundation for designing ferroelectric memories.

cond-mat.mtrl-sci↗