A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

TL;DR

This paper presents a convolutional neural network trained on simulated 3D diffraction patterns to accurately identify and classify dislocations in crystalline materials, advancing defect detection in coherent diffraction imaging.

Contribution

It introduces a novel deep learning approach using simulated data to detect and classify dislocations in 3D diffraction patterns, improving defect identification in materials science.

Findings

Neural network accurately predicts dislocation presence or absence.

The model distinguishes between screw and edge dislocations.

Simulated data effectively trains the neural network for defect classification.

Abstract

Coherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials.These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type(screw or edge). Our study paves the way for defect recognition in 3D coherent diffraction patterns for material science