Insightful classification of crystal structures using deep learning.

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 networ

SUBMITTER: Ziletti A 

PROVIDER: S-EPMC6050314 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature