Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

TL;DR

This paper introduces a hierarchical visualization framework using graph convolutional neural networks to explore and understand complex materials spaces by revealing patterns at multiple scales across different material classes.

Contribution

The work presents a novel unified approach for visualizing materials space hierarchically using GCNs, capturing compositional and structural similarities at various levels.

Findings

Patterns reflect multiple scales of similarity in materials.

Elemental and structural motifs are automatically identified.

The method aids data-centered exploration in materials design.

Abstract

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.