Morphology prediction of small nanoparticles in any orientation from single electron micrographs
Source abstract
Abstract
Abstract Accurate and automated data analysis for transmission electron microscopy will enable new high-throughput experiments that can reveal atomic-scale structure–property relationships for many functional materials. A key challenge in this pursuit is scalable three-dimensional structure prediction from single two-dimensional images. Existing tomographic and atom-counting approaches require either high electron doses, complex acquisition schemes, or the object in specific orientations, limiting experimental design. Here, we introduce a diffusion-based generative workflow that predicts the three-dimensional morphology of nanoscale objects directly from a single scanning/transmission electron micrograph. Applied to sub-5 nm platinum nanoparticles on ceria, it successfully predicts reasonable structures across diverse particle morphologies and imaging orientations. Combined with automated data acquisition in operando experiments, we believe techniques like this could be an essential part in relating ensemble-level structural variation and dynamics with performance, particularly fitting for heterogeneous catalysis.
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Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Machine Learning in Materials Science · Advanced Materials Characterization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
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