ArticleScience advances2023
Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Scaling multimodal materials representation learning with synthetic narratives.Nature communications · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- A deep learning model for predicting daily PMScience advances · 2026Article
- A partially quantum‑informed crystal graph network for direct adsorption energy prediction in catalysis.Scientific reports · 2026Article
- Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Journal of chemical information and modeling · 2026Article
- CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.Nature communications · 2026Article
- A crystal graph to vector approach for predicting magnetic properties.Scientific reports · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Band-Gap Regression with Architecture-Optimized Message-Passing Neural Networks.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.Journal of chemical theory and computation · 2025Article
- Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions.npj computational materials · 2025Article
- Topological Characterization of Metal-Organic Frameworks: A Perspective.Chemistry of materials : a publication of the American Chemical Society · 2024Review
- Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances.Science progressReview
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Authors and funding
7 authors.
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Abstract
Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.
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