@article {aSole26, title = {PRISM: periodic representation with multiscale and similarity graph modelling for enhanced crystal structure property prediction}, journal = {npj Computational Materials}, year = {2026}, month = {04/2026}, 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. The project page can be found at: https://imatge-upc.github.io/PRISM/.

}, doi = {https://doi.org/10.1038/s41524-026-02074-1}, author = {Sol{\'e}, A. and Mosella-Montoro, Albert and Cardona, J. and Aravena, D. and G{\'o}mez-Coca, S. and Ruiz, E. and Ruiz-Hidalgo, J.} }