  Machine-learned potentials (MLPs) have become a popular approach of modelling
interatomic interactions in atomistic simulations, but to keep the
computational cost under control, a relatively short cutoff must be imposed,
which put serious restrictions on the capability of the MLPs for modelling
relatively long-ranged dispersion interactions. In this paper, we propose to
combine the neuroevolution potential (NEP) with the popular D3 correction to
achieve a unified NEP-D3 model that can simultaneously model relatively
short-ranged bonded interactions and relatively long-ranged dispersion
interactions. We show the improved descriptions of the binding and sliding
energies in bilayer graphene can be obtained by the NEP-D3 approach compared to
the pure NEP approach. We implement the D3 part into the GPUMD package such
that it can be used out of the box for many exchange-correlation functionals.
As a realistic application, we show that dispersion interactions result in
approximately a 10% reduction in thermal conductivity for three typical
metal-organic frameworks.
