Insightful classification of crystal structures using deep learning

By Angelo Ziletti, Devinder Kumar, Matthias Scheffler, Luca M. Ghiringhelli. Nature Communications, 2017

323 citations recorded by the source corpus.

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. Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.

Subfields: Materials Chemistry, Structural Biology

View Paper: https://www.semanticscholar.org/paper/fb92e99d7e043b9e2e3da868daef32d6aa690175

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Rohit Batra
2. Rampi Ramprasad
3. Le Song
4. Chaopeng Shen
5. Yuri Mishin
6. Nour Makke
7. Sanjay Chawla

In Structural Biology

1. Chaopeng Shen

Citations in the corpus

• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “Ziletti et al. (2017) used the same approach to learn about crystal structures that are used by the network to classify crystals.”
• Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “Several tertiary property models can also be formulated as classification problems, such as stable versus unstable perovskite structures 84,113 or determining the most likely lattice symmetry of a defect-containing structure 114 .”