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Site-Net is a transformer architecture that models the periodic crystalstructures of inorganic materials as a labelled point set of atoms and reliesentirely on global self-attention and geometric information to guide learning.Site-Net processes standard crystallographic information files to generate alarge real-space supercell, and the importance of interactions between allatomic sites is flexibly learned by the model for the prediction taskpresented. The attention mechanism is probed to reveal Site-Net can learnlong-range interactions in crystal structures, and that specific attentionheads become specialized to deal with primarily short- or long-rangeinteractions. We perform a preliminary hyperparameter search and train Site-Netusing a single graphics processing unit (GPU), and show Site-Net achievesstate-of-the-art performance on a standard band gap regression task.

Site-Net: Using global self-attention and real-space supercells to capture long-range interactions in crystal structures