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ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements of atoms, requiring methods capable of capturing both local and global information effectively. However, most current works fall short of capturing long-range interactions within periodic structures. To address this limitation, we leverage \emph{reciprocal space} to efficiently encode long-range interactions with learnable filters within Fourier transforms. We introduce Reciprocal Geometry Network (ReGNet), a novel architecture that integrates geometric GNNs and reciprocal blocks to model short-range and long-range interactions, respectively. Experimental results on JARVIS, Materials Project, and MatBench demonstrate that ReGNet achieves state-of-the-art predictive accuracy across a range of crystal property prediction tasks. Additionally, we explore a model extension that employs the mixture-of-experts for multi-property prediction with promising results and high computational efficiency. These findings highlight the potential of our model as a scalable and accurate solution for crystal property prediction. The code will be released upon paper acceptance.

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