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.
@article{arxiv.2511.20362,
title = {PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction},
author = {Àlex Solé and Albert Mosella-Montoro and Joan Cardona and Daniel Aravena and Silvia Gómez-Coca and Eliseo Ruiz and Javier Ruiz-Hidalgo},
journal= {arXiv preprint arXiv:2511.20362},
year = {2025}
}