  Interfaces play a crucial role in determining the overall performance and
functionality of electronic devices and systems. Driven by the data science,
machine learning (ML) reveals excellent guidance for material selection and
device design, in which an advanced database is crucial for training models
with state-of-the-art (SOTA) precision. However, a systematic database of
interfaces is still in its infancy due to the difficulties in collecting raw
data in experiment and the expensive first-principles computational cost in
density functional theory (DFT). In this paper, we construct ample interface
structures of graphene nanoribbons (GNR), whose interfacial morphology can be
precisely fabricated based on specific molecular precursors. The GNR interfaces
serve as promising candidates since their bandgaps can be modulated. Their
physical properties including energy bands and density of states (DOS) maps are
obtained under reasonable calculation parameters. This database can provide
theoretical guidance for the design of electronic devices and accelerate the ML
study of various physical quantities.
