A convolutional neural network for defect classification in | ResearchBunny

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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

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