Machine-learning (ML) algorithms are leading the current revolution in the field of computational chemistry. Among the wide variety of ML methods developed for computational chemistry, neural network potentials have reached accuracy levels comparable to high level quantum mechanic (QM) based methods in the prediction of energies and forces, but orders of magnitude faster. However, most of them are limited to the prediction of energies and forces of isolated, in vacuo, systems.