Insightful classification of crystal structures using deep learning

Nature Communications
Angelo ZilettiLuca M Ghiringhelli

Abstract

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep learning neural network model for classification. Our approach is able to correctly classify a dataset comprising more than 100,000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal structure recognition of-possibly noisy and incomplete-three-dimensional structural data in big-data materials science.

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Citations

Jul 3, 2019·Acta Crystallographica. Section A, Foundations and Advances·Chia Hao LiuSimon J L Billinge
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Nov 22, 2019·Nature Communications·Ying ZhangChen Ling
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Methods Mentioned

BETA
x-ray diffraction
electron diffraction

Software Mentioned

Spglib
AFLOW Library
Python Materials Genomics ( pymatgen )
NOMAD Analytics Toolkit
ConvNets
Platon
pymatgen )
SYM
AFLOW Library of Crystallographic Prototypes
Condor

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