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Potentials that could accurately describe the irradiation damage processesare highly desired to figure out the atomic-level response of variousnewly-discovered materials under irradiation environments. In this work, weintroduce a deep-learning interatomic potential for monolayer MoS2 by combiningall-electron calculations, an active-learning sampling method and a hybriddeep-learning model. This potential could not only give an overall goodperformance on the predictions of near-equilibrium material propertiesincluding lattice constants, elastic coefficients, energy stress curves, phononspectra, defect formation energy and displacement threshold, but also reproducethe ab initial irradiation damage processes with high quality. Furtherirradiation simulations indicate that one single highenergy ion could generatea large nanopore with a diameter of more than 2 nm, or a series of multiplenanopores, which is qualitatively verified by the subsequent 500 keV Au+ ionirradiation experiments. This work provides a promising and feasible approachto simulate irradiation effects in enormous newly-discovered materials withunprecedented accuracy.

Deep-learning interatomic potential for irradiation damage simulations in MoS2 with ab initial accuracy