  We propose a hybrid scheme that interpolates smoothly the
Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a
newly developed deep learning potential energy model. The resulting DP-ZBL
model can not only provide overall good performance on the predictions of
near-equilibrium material properties but also capture the right physics when
atoms are extremely close to each other, an event that frequently happens in
computational simulations of irradiation damage events. We applied this scheme
to the simulation of the irradiation damage processes in the
face-centered-cubic aluminium system, and found better descriptions in terms of
the defect formation energy, evolution of collision cascades, displacement
threshold energy, and residual point defects, than the widely-adopted ZBL
modified embedded atom method potentials and its variants. Our work provides a
reliable and feasible scheme to accurately simulate the irradiation damage
processes and opens up new opportunities to solve the predicament of lacking
accurate potentials for enormous newly-discovered materials in the irradiation
effect field.
