Machine learning for experimental design of ultrafast electron diffraction

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Machine learning
Ultrafast electron diffraction
Variational autoencoder
Self-supervised learning
Experimental design
Online Access:https://doi.org/10.1038/s41598-025-06779-z
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-06779-z

Similar Items