PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911286286090240 |
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| author | Solé, Àlex Mosella-Montoro, Albert Cardona, Joan Aravena, Daniel Gómez-Coca, Silvia Ruiz, Eliseo Ruiz-Hidalgo, Javier |
| author_facet | Solé, Àlex Mosella-Montoro, Albert Cardona, Joan Aravena, Daniel Gómez-Coca, Silvia Ruiz, Eliseo Ruiz-Hidalgo, Javier |
| contents | Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction Solé, Àlex Mosella-Montoro, Albert Cardona, Joan Aravena, Daniel Gómez-Coca, Silvia Ruiz, Eliseo Ruiz-Hidalgo, Javier Machine Learning Materials Science Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction. |
| title | PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2511.20362 |