Materials design in the quantum regime call for the integration of multi-tier materials information that go beyond atomic structures. Especially, many quantum behaviors are greatly controlled by local symmetries and local bonding environments. In this talk, motivated by Pauling’s rules, Dr. Yan will show that local bonding environments (motifs) can be incorporated in a graph-based machine learning architecture to make reliable property predictions for solid-state quantum materials including complex metal oxides. The proposed atom-motif dual network model demonstrates the feasibility to incorporate beyond-atom materials information in a graph network framework and achieves the state-of-the-art performance in predicting the electronic structure properties of complex metal oxides. As an example of quantum material design by local bonding symmetry, he will discuss how data-driven material science can be combined with symmetry-based physical principles to guide the search for quantum defects in two-dimensional (2D) materials for quantum information processing and quantum computing. The use of local bonding symmetry (irreducible representations) as a material design hypothesis enables the identification of anion antisite defects as promising spin qubits and quantum emitters in six monolayer transition metal dichalcogenides. The work creates a technically accessible 2D platform for the fabrication of defect-based multi-qubit systems for quantum computing. At the end of the talk, he will discuss the continued development of machine learning models that embrace symmetries and symmetry-based interactions and the discovery of quantum defects in a vast space of 2D material systems.