Broader impact Molecular machine learning is a critical component of pipelines for drug and material discovery, catalyst optimization, and a valuable tool for studying complex biochemical processes. Infusion of electronic data into graph representations for molecular ML will increase trust in these algorithms, contribute to increased interpretability of the models, and open new opportunities to research the relationship between electronic structure and molecular properties. Moreover, this work can also be used for the theoretical chemistry community once it allows high-throughput NBO analysis. The predicted orbitals can be applied to analyze chemical reactivity in a wide range of systems.