  Predicting material properties base on micro structure of materials has long
been a challenging problem. Recently many deep learning methods have been
developed for material property prediction. In this study, we propose a crystal
representation learning framework, Orbital CrystalNet, OCrystalNet, which
consists of two parts: atomic descriptor generation and graph representation
learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and
atomic features to construct OFM-feature atomic descriptor, and then the atomic
descriptor is used as atom embedding in the atom-bond message passing module
which takes advantage of the topological structure of crystal graphs to learn
crystal representation. To demonstrate the capabilities of OCrystalNet we
performed a number of prediction tasks on Material Project dataset and JARVIS
dataset and compared our model with other baselines and state of art methods.
To further present the effectiveness of OCrystalNet, we conducted ablation
study and case study of our model. The results show that our model have various
advantages over other state of art models.
