Articlenpj computational materials2026
Morphology prediction of small nanoparticles in any orientation from single electron micrographs.
Article in npj computational materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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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
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