  Moir\'e-twisted materials have garnered significant research interest due to
their distinctive properties and intriguing physics. However, conducting
first-principles studies on such materials faces challenges, notably the
formidable computational cost associated with simulating ultra-large twisted
structures. This obstacle impedes the construction of a twisted materials
database crucial for datadriven materials discovery. Here, by using
high-throughput calculations and state-of-the-art neural network methods, we
construct a Deep-learning Database of density functional theory (DFT)
Hamiltonians for Twisted materials named DDHT. The DDHT database comprises
trained neural-network models of over a hundred homo-bilayer and hetero-bilayer
moir\'e-twisted materials. These models enable accurate prediction of the DFT
Hamiltonian for these materials across arbitrary twist angles, with an averaged
mean absolute error of approximately 1.0 meV or lower. The database facilitates
the exploration of flat bands and correlated materials platforms within
ultra-large twisted structures.
