Towards automating structural discovery in scanning transmission electron microscopy *

A Platform for Atomic Fabrication and In Situ Synthesis in a Scanning Transmission Electron Microscope

The engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Atomic-scale modification will be a key enabling factor for engineering quantum materials where desired phenomena are critically determined by atomic structures. Successful use of scanning transmission electron microscopes (STEMs) for atomic scale material manipulation has opened the door for a transformed view of what can be accomplished using electron-beam-based strategies. However, serious obstacles exist on the pathway from possibility to practical reality. One such obstacle is the in situ delivery of atomized material in the STEM to the region of interest for further fabrication processes. Here, progress on this front is presented with a view toward performing synthesis (deposition and growth) processes in a scanning transmission electron microscope in combination with top-down control over the reaction region. An in situ thermal deposition platform is presented, tested, and deposition and growth processes are demonstrated. In particular, it is shown that isolated Sn atoms can be evaporated from a filament and caught on the nearby sample, demonstrating atomized material delivery. This platform is envisioned to facilitate real-time atomic resolution imaging of growth processes and open new pathways toward atomic fabrication.

Applications of Transmission Electron Microscopy in Phase Engineering of Nanomaterials

Phase engineering of nanomaterials (PEN) is an emerging field that aims to tailor the physicochemical properties of nanomaterials by precisely manipulating their crystal phases. To advance PEN effectively, it is vital to possess the capability of characterizing the structures and compositions of nanomaterials with precision. Transmission electron microscopy (TEM) is a versatile tool that combines reciprocal-space diffraction, real-space imaging, and spectroscopic techniques, allowing for comprehensive characterization with exceptional resolution in the domains of time, space, momentum, and, increasingly, even energy. In this Review, we first introduce the fundamental mechanisms behind various TEM-related techniques, along with their respective application scopes and limitations. Subsequently, we review notable applications of TEM in PEN research, including applications in fields such as metallic nanostructures, carbon allotropes, low-dimensional materials, and nanoporous materials. Specifically, we underscore its efficacy in phase identification, composition and chemical state analysis, in situ observations of phase evolution, as well as the challenges encountered when dealing with beam-sensitive materials. Furthermore, we discuss the potential generation of artifacts during TEM imaging, particularly in scanning modes, and propose methods to minimize their occurrence. Finally, we offer our insights into the present state and future trends of this field, discussing emerging technologies including four-dimensional scanning TEM, three-dimensional atomic-resolution imaging, and electron microscopy automation while highlighting the significance and feasibility of these advancements.

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

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 MnPS₃ , 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.

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. The discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.