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Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery., (© 2024 Wiley‐VCH GmbH.)

Interfaces in energy materials and devices often involve beam-sensitive materials such as fast ionic, soft, or liquid phases. 4D scanning transmission electron microscopy (4D-STEM) offers insights into local lattice, strain charge, and field distributions, but faces challenges in analyzing beam-sensitive interfaces at high spatial resolutions. Here, a 4D-STEM compressive sensing algorithm is introduced that significantly reduces data acquisition time and electron dose. This method autonomously allocates probe positions on interfaces and reconstructs missing information from datasets acquired via dynamic sampling. This algorithm allows for the integration of various scanning schemes and electron probe conditions to optimize data integrity. Its data reconstruction employs a neural network and an autoencoder to correlate diffraction pattern features with measured properties, significantly reducing training costs. The accuracy of the reconstructed 4D-STEM datasets is verified using a combination of explicitly and implicitly trained parameters from atomic resolution datasets. This method is broadly applicable for 4D-STEM imaging of any local features of interest and will be available on GitHub upon publication., (© 2024 Wiley‐VCH GmbH.)

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD., (© 2024 Wiley‐VCH GmbH.)

This study introduces the integration of dynamic computer vision-enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)-based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

The impulsive excitation of ensembles of excitons or color centers by a high-energy electron beam results in the observation of photon bunching in the second-order correlation function of the cathodoluminescence generated by those emitters. Photon bunching in cathodoluminescence microscopy can be used to resolve the excited-state dynamics and the excitation and emission efficiency of nanoscale materials, and it can be used to probe interactions between emitters and nanophotonic cavities. Unfortunately, the required integration times for these measurements can be problematic for beam-sensitive materials. Here, we report substantial changes in the measured bunching induced by indirect electron interactions (with indirect electron excitation inducing g 2 (0) values approaching 10 4 ). This result is critical to the interpretation of g 2 ( τ ) in cathodoluminescence microscopies, and, more importantly, it provides a foundation for the nanoscale characterization of optical properties in beam-sensitive materials.

Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS 3 , a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

Ordered mesoscale structures in 2D materials induced by small misorientations have allowed for a wide variety of electronic, ferroelectric, and quantum phenomena to be explored. Until now, the only mechanism to induce this periodic ordering was via mechanical rotations between the layers, with the periodicity of the resulting moiré pattern being directly related to twist angle. Here we report a fundamentally distinct mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorus trichalcogenide, MnPS 3 , induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulfur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and enables device applications by enabling controlled periodic potentials on the atomic scale.

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and "intelligent" probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry, and materials and are limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, the atomic-sized probe of the scanning transmission electron microscope is used to perform precise sculpting and design nanoparticle configurations. Using low-loss EELS, dynamic analyses of the evolution of the plasmonic response are provided. It is shown that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy. This process significantly increases the scope for design possibilities and presents opportunities for unique structure development, which are ultimately the key for nanophotonic design., (© 2021 Wiley-VCH GmbH.)

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. The analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data., (Published by Elsevier B.V.)

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co 3 Sn 2 S 2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co 3 Sn 2 S 2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

Design of nanoscale structures with desired optical properties is a key task for 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 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 local responses based on geometries for fixed compositions and surface chemical states. 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 yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures., (© 2021 Wiley-VCH GmbH.)

A synthetic challenge in faceted metal oxide nanocrystals (NCs) is realizing tunable localized surface plasmon resonance (LSPR) near-field response in the infrared (IR). Cube-shaped nanoparticles of noble metals exhibit LSPR spectral tunability limited to visible spectral range. Here, we describe the colloidal synthesis of fluorine, tin codoped indium oxide (F,Sn:In 2 O 3 ) NC cubes with tunable IR range LSPR for around 10 nm particle sizes. Free carrier concentration is tuned through controlled Sn dopant incorporation, where Sn is an aliovalent n-type dopant in the In 2 O 3 lattice. F shapes the NC morphology into cubes by functioning as a surfactant on the {100} crystallographic facets. Cube shaped F,Sn:In 2 O 3 NCs exhibit narrow, shape-dependent multimodal LSPR due to corner, edge, and face centered modes. Monolayer NC arrays are fabricated through a liquid-air interface assembly, further demonstrating tunable LSPR response as NC film nanocavities that can heighten near-field enhancement (NFE). The tunable F,Sn:In 2 O 3 NC near-field is coupled with PbS quantum dots, via the Purcell effect. The detuning frequency between the nanocavity and exciton is varied, resulting in IR near-field dependent enhanced exciton lifetime decay. LSPR near-field tunability is directly visualized through IR range scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS). STEM-EELS mapping of the spatially confined near-field in the F,Sn:In 2 O 3 NC array interparticle gap demonstrates elevated NFE tunability in the arrays.

A dense array of vertically aligned indium antimonide (InSb) nanowires with high aspect ratio (diameter 150 nm, length 20 μ m) were grown in the pores of a track-etched polycarbonate membrane via a one-step electrochemical method. There are several reports on InSb nanowire growth in the pores of a mechanically rigid, nano-channel alumina template (NCA), where nanowire growth occurs in the pores of the NCA. This work on InSb nanowire growth in pores of track-etched polycarbonate (PC) membrane sheds light on the various factors that affect nucleation and nanowire growth. The average length and diameter of the as-grown nanowires was about 10 μ m and 150 nm, respectively. Two possible mechanisms accounting for two different morphologies of the as-grown nanowires are proposed. The polycrystallinity observed in some of the nanowires is explained using the 3D 'nucleation-coalescence' mechanism. On the other hand, single crystal nanowires with a high density of twin defects and stacking faults grow epitaxially by a two-dimensional (2D) nucleation/growth mechanism. To assess the electrical quality of the nanowires, two- and four-terminal devices were fabricated using a single InSb nanowire contacted by two Ni electrodes. It was found that, at low bias, the ohmic current is controlled by charge diffusion from the bulk contacts. On the other hand, at high bias, the effects of space charge limited current (SCLC) are evident in the current-voltage behavior, characteristic of transport through structures with reduced electrostatic screening. A cross-over from ohmic to SCLC occurs at about 0.14 V, yielding a free carrier concentration of the order of 10 14 cm - 3 .

Gratings with complex multilayer strips are studied under inclined incident light. Great interest in these gratings is due to applications as input/output tools for waveguides and as subwavelength metafilms. The structured strips introduce anisotropy in the effective parameters, providing additional flexibility in polarization and angular dependences of optical responses. Their characterization is challenging in the intermediate regime between subwavelength and diffractive modes. The transition between modes occurs at the Wood's anomaly wavelength, which is different at different angle of incidence. The usual characterization with an effective film using permittivity ε and permeability μ has limited effectiveness at normal incidence but does not apply at inclined illumination, due to the effect of periodicity. The optical properties are better characterized with effective medium strips instead of an effective medium layer to account for the multilayer strips and the underlying periodic nature of the grating. This approach is convenient for describing such intermediate gratings for two types of applications: both metafilms and the coupling of incident waves to waveguide modes or diffraction orders. The parameters of the effective strips are retrieved by matching the spectral-angular map at different incident angles., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.