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arXiv · 2511.20362

PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction

Àlex Solé·Albert Mosella-Montoro·Joan Cardona·Daniel Aravena·Silvia Gómez-Coca·Eliseo Ruiz·Javier Ruiz-Hidalgo
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Abstract

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.

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Àlex Solé, Albert Mosella-Montoro, Joan Cardona, Daniel Aravena, Silvia Gómez-Coca, Eliseo Ruiz, Javier Ruiz-Hidalgo. 2025-11-25. PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction. https://arxiv.org/abs/2511.20362

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