Node-level cross-attention for multimodal crystal property prediction using structural graphs and textual descriptions
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Abstract
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Research context
OpenAlex topics: Machine Learning in Materials Science · Crystallography and molecular interactions · Advanced Graph Neural Networks
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References (12)
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1. Graph networks as a universal machine learning framework for molecules and crystals10.1021/acs.chemmater.9b01294 ↗
2. Lin · 2023
3. Periodic graph transformers for crystal material property prediction10.52202/068431-1096 ↗
4. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties10.1103/PhysRevLett.120.145301 ↗
5. Nicholas, W., Amalie, T., Haoyan, H., Sanghoon, L., Kevin, C., John, D., Alexander, D., Kristin, P., Gerbrand, C., & Anubhav, J. (2021). The impact of domain-specific pre-training on named entity recognition tasks in materials science. Available at SSRN 3950755.
6. LLM-prop: predicting the properties of crystalline materials using large language models10.1038/s41524-025-01536-2 ↗
7. Cast: cross attention based multimodal fusion of structure and text for materials property prediction
8. Graph isomorphism attention network combined with pre-trained language models: a novel approach for crystal material property prediction10.1007/s00521-025-11532-8 ↗
9. MatMMFuse: multimodal fusion model for material property prediction
10. Xval: a continuous number encoding for large language models
11. Exploring the limits of transfer learning with a unified text-to-text transformer
12. Scalable deeper graph neural networks for high-performance materials property prediction10.1016/j.patter.2022.100491 ↗