Recently, graph neural network (GNN) has shown state-of-the-art performance on molecules and materials. Molecular and Crystal graph neural network, as an end-to-end learned natural descriptor for crystal, has been well developed these years . It has shown outstanding ability in training potential, interpreting materials, predicting properties, and inverse design . The 3D geometric structure of materials can be described as a connected graph. Structure representations are learned directly from the input structure using a more natural method without manual construction as a graph learning architecture. SE(3) symmetry is naturally conserved in the graph representations of GNN including translation, rotation, and permutation. The most recent state of art GNN is E(3)-equivariant graph neural networks (ENN) including NequIP and Allegro , utilizing the E(3)-equivariant representations and operations of the geometric tensors based on the Euclidean Neural Networks (e3nn) . Compared to general invariant GNN, the parity symmetry and high order quantities are employed in ENN and help to express the molecular and material graph.