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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Unravelling the components of diffuse scattering using deep learning.

Chloe A Fuller¹, Lucas S P Rudden²

¹Swiss-Norwegian Beamlines, ESRF, Grenoble, France.

|November 14, 2023

Summary

This summary is machine-generated.

A new deep-learning method, DSFU-Net, effectively separates diffuse scattering data into molecular form factor and chemical short-range order components. This advances the analysis of local structures in functional materials.

Keywords:

Pix2Pix generative adversarial networks computational modelling deep learning diffuse scattering disorder molecular crystals molecular form factors short-range order

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Area of Science:

Materials Science
Crystallography
Computational Materials Science

Background:

Technologically important material properties depend on both average and local structures.
Local structure information is present in diffuse scattering, but analysis is challenging, especially for single crystals.
Separating diffuse scattering components simplifies analysis and allows quantitative disorder parameter extraction.

Purpose of the Study:

To develop a deep-learning method for factorizing diffuse scattering into molecular form factor and chemical short-range order contributions.
To streamline the analysis of single-crystal diffuse scattering data.
To enable automated workflows for structural analysis.

Main Methods:

A deep-learning model, DSFU-Net, based on the Pix2Pix generative adversarial network was developed.
DSFU-Net was trained on a large dataset of simulated diffuse scattering data (198,421 samples).
The method was validated on unseen simulated data and a real experimental example.

Main Results:

DSFU-Net successfully factorized diffuse scattering into molecular form factor and chemical short-range order components.
The method achieved high performance on simulated validation datasets.
On experimental data, DSFU-Net distinguished between structural models and refined short-range-order parameters, comparable to established methods.

Conclusions:

DSFU-Net offers a streamlined approach to diffuse scattering analysis with minimal prior knowledge.
The method provides rapid access to both scattering components and can handle missing data.
DSFU-Net represents a significant step towards automated analysis of single-crystal diffuse scattering.