  Accurate prediction for the electronic structure properties of halide
perovskites plays a significant role in the design of highly efficient and
stable solar cells. While density functional theory (DFT) within the
generalized gradient approximation (GGA) offers reliable prediction in terms of
lattice constants and potential energy surface for halide perovskites, it
severely underestimates the band gap due to the lack of non-local exact
exchange term, which exists in computationally expensive hybrid functionals. In
this work, a universal Deep Kohn-Sham (DeePKS) model based on neural network is
trained so as to enable electronic structure calculations with the accuracy of
hybrid functional HSE06 and the efficiency comparable to GGA functional, for a
plethora of halide perovskites, i.e., ABX$_3$ (A=FA, MA, Cs; B=Sn, Pb; X=Cl,
Br, I). Forces, band gaps, and density of states (DOS) predicted by our DeePKS
model for all aforementioned perovskites are in good agreement with the HSE06
results, with significantly improved efficiency. In addition, even though the
spin-orbit coupling (SOC) effect has not been taken into consideration during
the training process, DeePKS+SOC offers highly consistent band gap and DOS as
compared to HSE06+SOC for Pb-containing systems. We believe such DeePKS model
can be readily applied for an accurate yet efficient prediction of various
properties for the family of halide perovskites.
