Improving crystal material property prediction with multi-view geometric graph transformer

Accurately and comprehensively representing crystal structures is critical for advancing machine learning in large-scale crystal materials simulations. However, effectively capturing and leveraging the intricate geometric and topological characteristics of crystal structures remains a significant challenge for most existing methods in crystal property prediction. Here, we propose MGT, a multi-view graph transformer that jointly models SE(3) invariant scalar representations and SO(3) equivariant directional representations, enabling the capture of both rotational-translational invariance and rotation-equivariant directional information in crystal structures. A mixture of experts inspired router serves as the key integration mechanism, adaptively weighting these complementary embeddings for each target task. Through multi-task self-supervised pretraining, MGT achieves up to 14% reduction in mean absolute error compared with previous state-of-the-art models on crystal property benchmarks. Comprehensive ablation and interpretability analyses confirm that both the self-supervised pretraining strategy and the mixture of experts inspired router contribute to the overall model performance. In transfer learning scenarios-including crystal catalyst adsorption energy and hybrid perovskite bandgap prediction-MGT achieves performance improvements of up to 58% over existing baselines, demonstrating strong domain-agnostic scalability. Overall, all the results suggest that MGT is an effective and generalizable framework for crystal material property prediction, with significant potential to accelerate the discovery of novel materials.