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

• URL: http://arxiv.org/abs/2511.20362v1
• Date: Tue, 25 Nov 2025 14:43:14 GMT
• Title: PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction
• Authors: Àlex Solé, Albert Mosella-Montoro, Joan Cardona, Daniel Aravena, Silvia Gómez-Coca, Eliseo Ruiz, Javier Ruiz-Hidalgo,
• Abstract summary: PRISM is a graph neural network framework that integrates multiscale representations and periodic feature encoding.<n>Experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy.
• Score: 3.039926847552439
• License: http://creativecommons.org/licenses/by/4.0/
• 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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