A convolutional neural network for defect classification in | ResearchBunny

Index

Abstract

This paper introduces a 3D convolutional neural network (CNN) for rapid and accurate defect classification in nanocrystals using Bragg coherent X-ray diffraction patterns (CXDPs). The CNN is trained on simulated CXDPs generated from thousands of atomistic configurations of nanocrystals with various defects (screw and edge dislocations). The network successfully predicts the presence and type of dislocations, paving the way for automated defect recognition in materials science.

Publisher

npj Computational Materials

Published On

Jul 21, 2021

Authors

Bruce Lim, Ewen Bellec, Maxime Dupraz, Steven Leake, Andrea Resta, Alessandro Coati, Michael Sprung, Ehud Almog, Eugen Rabkin, Tobias Schülli, Marie-Ingrid Richard

Related Publications

Explore these studies to deepen your understanding of the subject.

$A deep convolutional neural network for real-time full profile analysis of big powder diffraction data$

Chemistry

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

H. Dong, K. T. Butler, et al.

$Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks$

Physics

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

L. Wu, S. Yoo, et al.

Medicine and Health

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Y. Kim, S. Kim, et al.

$Bragg glass signatures in PdxErTe3 with X-ray diffraction temperature clustering$

Physics

Bragg glass signatures in PdxErTe3 with X-ray diffraction temperature clustering

K. Mallayya, J. Straquadine, et al.

Listen, Learn & Level Up

Over 10,000 hours of research content in 25+ fields, available in 12+ languages.

No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.

listen to research audio papers with researchbunny