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

Engineering and Technology

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

B. Lim, E. Bellec, et al.

This paper highlights a groundbreaking 3D convolutional neural network (CNN) designed for swift and precise identification of defects in nanocrystals by analyzing Bragg coherent X-ray diffraction patterns. With training on extensive simulations, the CNN adeptly identifies dislocation types, marking a significant leap towards automated defect detection in materials science. This innovative research was conducted by Bruce Lim, Ewen Bellec, Maxime Dupraz, Steven Leake, Andrea Resta, Alessandro Coati, Michael Sprung, Ehud Almog, Eugen Rabkin, Tobias Schülli, and Marie-Ingrid Richard.

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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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