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Predicting material properties base on micro structure of materials has longbeen a challenging problem. Recently many deep learning methods have beendeveloped for material property prediction. In this study, we propose a crystalrepresentation learning framework, Orbital CrystalNet, OCrystalNet, whichconsists of two parts: atomic descriptor generation and graph representationlearning. In OCrystalNet, we first incorporate orbital field matrix (OFM) andatomic features to construct OFM-feature atomic descriptor, and then the atomicdescriptor is used as atom embedding in the atom-bond message passing modulewhich takes advantage of the topological structure of crystal graphs to learncrystal representation. To demonstrate the capabilities of OCrystalNet weperformed a number of prediction tasks on Material Project dataset and JARVISdataset and compared our model with other baselines and state of art methods.To further present the effectiveness of OCrystalNet, we conducted ablationstudy and case study of our model. The results show that our model have variousadvantages over other state of art models.

Leveraging Orbital Information and Atomic Feature in Deep Learning Model