Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

Insightful classification of crystal structures using deep learning

Ziletti A, Kumar D, Scheffler M, Ghiringhelli LM (2018)

---

Publication Type: Journal article

Publication year: 2018

Journal

Book Volume: 9

Article Number: 2775

Journal Issue: 1

DOI: 10.1038/s41467-018-05169-6

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.

Authors with CRIS profile

Involved external institutions

How to cite

APA:

Ziletti, A., Kumar, D., Scheffler, M., & Ghiringhelli, L.M. (2018). Insightful classification of crystal structures using deep learning. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05169-6

MLA:

Ziletti, Angelo, et al. "Insightful classification of crystal structures using deep learning." Nature Communications 9.1 (2018).

BibTeX: Download