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Sungin Kim

Publications and source records attributed to Sungin Kim.

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Visualizing Millisecond Atomic Dynamics of Nanocrystals in Liquid

Atomic structures of nanomaterials are inherently dynamic, continuously reshaped through interactions with chemical species and external stimuli. Such dynamics are further amplified as the size and dimensionality of nanomaterials are reduced. Despite advances in analytical methods, it remains challenging to capture structural dynamics of nanomaterials in reactive environments with both atomic spatial resolution and commensurate temporal resolution. Here, we directly visualize atomic-scale dynamics of gold (Au) nanocrystals in reactive liquid environments with millisecond-speed liquid cell electron microscopy (EM) and deep-learning denoising. We uncover reversible fluctuations in local crystallinity of Au nanocrystals dependent on the surrounding chemical environment. These transient fluctuations, driven by interactions at nanocrystal-liquid interfaces, critically influence dissolution kinetics and grain boundary relaxation. By overcoming the spatiotemporal limitations in conventional liquid cell EM, our findings provide insights into how transient nanoscale structures dictate the stability and reactivity of nanomaterials.

cond-mat.mtrl-sci

A Transformer-based Model for Rapid Microstructure Inference from Four-Dimensional Scanning Transmission Electron Microscopy Data

Properties of crystalline materials are closely linked to microstructure arising from the spatial arrangement, orientation, and phase of nanocrystals. Rapid characterization of crystalline microstructure can accelerate the identification of these links and the development of materials with desired properties. Here, we combine a machine learning framework with four-dimensional scanning transmission electron microscopy (4D-STEM) to enable fast inference of crystalline microstructure over large fields of view. The framework employs a transformer-based architecture to predict crystallographic orientations and phases from 4D-STEM diffraction patterns, yielding spatially resolved maps of microstructural features at the nanoscale. With this framework, crystallographic orientations are inferred up to two orders of magnitude faster than widely used correlative template-matching approaches. This capability enables high-throughput characterization of complex crystalline materials and facilitates the establishment of structure-property relationships central to materials design and optimization.

cond-mat.mtrl-sci