ArticleActa crystallographica. Section A, Foundations and advances2025
Machine learning for the classification of serial electron diffraction patterns: synthetic data.
Article in Acta crystallographica. Section A, Foundations and advances, 2025. 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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Abstract
Serial electron crystallography faces a fundamental challenge due to the flat Ewald sphere resulting from the short electron wavelength, leading to limited 3D information in individual patterns. Recently, an algorithm for unit-cell determination from zonal electron diffraction patterns (GM algorithm) [Miehe (1997). Ber. Dtsch. Miner. Ges. Beih. z. Eur. J. Miner. 9, 250; Gorelik et al. (2025). Acta Cryst. A81, 124-136] was introduced in the context of serial electron crystallography. This algorithm requires the extraction of 2D zonal patterns from the complete serial dataset. Here, we present a machine learning approach for pattern sorting and apply it initially to simulated electron diffraction patterns.
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