{"ID":2836999,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20362","arxiv_id":"2511.20362","title":"PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2511.20362","url_pdf":"https://arxiv.org/pdf/2511.20362v1","authors":"[\"Àlex Solé\",\"Albert Mosella-Montoro\",\"Joan Cardona\",\"Daniel Aravena\",\"Silvia Gómez-Coca\",\"Eliseo Ruiz\",\"Javier Ruiz-Hidalgo\"]","published":"2025-11-25T14:43:14Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.mtrl-sci\"]","methods":"[\"Graph Neural Network\"]","has_code":false}