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Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection

Julian Zimmermann¹, Fabien Beguet¹, Daniel Guthruf¹

¹ETH Zürich, Zürich, Switzerland.

Npj Computational Materials

|April 26, 2024

Summary

This study introduces an automated method for analyzing large datasets of coherent diffraction images, enabling faster structure determination of nanosized particles without expert labeling. The approach uses projection learning for efficient data analysis.

Keywords:

Characterization and analytical techniques Design, synthesis and processing Nanoparticles

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Area of Science:

Materials Science
Physics
Data Science

Background:

Coherent diffraction imaging (CDI) provides high-resolution, in-situ structural measurements of nanosized particles.
Advancements in X-ray sources generate massive datasets, posing significant analysis challenges.

Purpose of the Study:

To develop an automated method for analyzing large coherent diffraction imaging datasets.
To enable efficient structure determination without manual expert labeling.

Main Methods:

Introduced projection learning, extending self-supervised contrastive learning to CDI.
Achieved dimensionality reduction to create semantically meaningful data embeddings.

Main Results:

Demonstrated an automated approach for finding semantic similarities in diffraction images.
Generated embeddings that align with physical intuition, outperforming previous methods.

Conclusions:

The developed method significantly improves the analysis of large CDI datasets.
Paves the way for real-time, large-scale analysis in X-ray free-electron laser experiments.