Machine learning for experimental design of ultrafast electron diffraction

Abstract Ultrafast electron diffraction (UED) experiments can extract insights into material behavior at ultrafast timescales but are limited by the manual analysis required to process several gigabytes of diffraction pattern data. The lack of real-time data prevents in situ tuning of experimental p...

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Detalles Bibliográficos

Principais autores:	Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2025-07-01
Series:	Scientific Reports
Assuntos:	Machine learning Ultrafast electron diffraction Variational autoencoder Self-supervised learning Experimental design
Acceso en liña:	https://doi.org/10.1038/s41598-025-06779-z
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