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Hierarchical visualization of materials space with graph convolutional neural networks
Tian Xie1, Jeffrey C Grossman1
1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
The Journal of Chemical Physics
|November 10, 2018
Summary
This study introduces a new framework for visualizing materials data, enabling efficient exploration of vast chemical and structural spaces. The approach automatically reveals patterns, aiding in automated materials design and discovery.
Area of Science:
- Materials Science
- Computational Chemistry
- Data Science
Background:
- High-throughput computation and machine learning accelerate materials design by screening extensive structural, chemical, and property spaces.
- The vast data generated necessitates advanced techniques for efficient exploration and pattern identification in materials science.
Purpose of the Study:
- To develop a unified framework for hierarchical visualization of compositional and structural similarities in materials.
- To enable efficient exploration and pattern discovery within large, complex materials datasets.
Main Methods:
- Utilized graph convolutional neural networks to learn representations of materials from different network layers.
- Developed a unified framework for hierarchical visualization of material similarities.
- Applied the framework to perovskites, elemental boron, and general inorganic crystals.
Main Results:
- Demonstrated automatic emergence of patterns reflecting similarities at various scales across different material classes.
- Identified elemental similarities in perovskites related to atomic properties.
- Revealed characteristic structural motifs and local coordination environments in elemental boron and inorganic crystals.
Conclusions:
- The developed visualization framework effectively identifies underlying patterns and similarities in diverse materials.
- This data-centered approach facilitates automated materials design and accelerates the discovery of novel materials.
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