  Machine learning potentials (MLPs) trained on data from quantum-mechanics
based first-principles methods can approach the accuracy of the reference
method at a fraction of the computational cost. To facilitate efficient
MLP-based molecular dynamics (MD) and Monte Carlo (MC) simulations, an
integration of the MLPs with sampling software is needed. Here we develop two
interfaces that link the Atomic Energy Network ({\ae}net) MLP package with the
popular sampling packages TINKER and LAMMPS. The three packages, {\ae}net,
TINKER, and LAMMPS, are free and open-source software that enable, in
combination, accurate simulations of large and complex systems with low
computational cost that scales linearly with the number of atoms. Scaling tests
show that the parallel efficiency of the {\ae}net-TINKER interface is nearly
optimal but is limited to shared-memory systems. The {\ae}net-LAMMPS interface
achieves excellent parallel efficiency on highly parallel distributed memory
systems and benefits from the highly optimized neighbor list implemented in
LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant
example applications, the investigation of diffusion phenomena in liquid water
and the equilibration of nanostructured amorphous battery materials.
