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 and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, 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 ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–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.

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Code availability: The ænet source code and its documentation are openly available from the ænet website (http://ann.atomistic.net) or from GitHub (https://github.com/atomisticnet/aenet). The implementation and examples, including ANN potentials, for crystalline/amorphous LiSi and liquid water are also openly available on GitHub at: 1. ænet–LAMMPS code (https://github.com/atomisticnet/aenet-lammps); 2. ænet–TINKER code (https://github.com/atomisticnet/aenet-tinker).

85.

Tutorials: Jupyter notebooks with tutorials demonstrating the usage of the TINKER and LAMMPS interfaces for three different example systems can be found at

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