ausblenden:
Schlagwörter:
Condensed Matter, Materials Science, Condensed Matter, Disordered Systems and Neural Networks
Zusammenfassung:
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 new
machine-learning-based approach to automatically classify structures by crystal
symmetry. First, we represent crystals by a diffraction image, and then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 80,000 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 in computational and experimental big-data materials science.
