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.
Zhang et al. (Wed,) studied this question.
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