# Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1807.03404 · HTML: https://mlsanity.com/paper/1807.03404/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Materials Representation and Transfer Learning for Multi-Property Prediction](https://mlsanity.com/paper/2106.02225/) (2021)
- [Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science](https://mlsanity.com/paper/2501.12919/) (2025)
- [Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics](https://mlsanity.com/paper/2501.02932/) (2025)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [Towards scientific machine learning for granular material simulations -- challenges and opportunities](https://mlsanity.com/paper/2504.08766/) (2025)
- [Substitutional Alloying Using Crystal Graph Neural Networks](https://mlsanity.com/paper/2306.10766/) (2023)
- [A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery](https://mlsanity.com/paper/2503.07378/) (2025)

## Abstract (arXiv)

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.