  Molecular dynamics simulations are an important tool for describing the
evolution of a chemical system with time. However, these simulations are
inherently held back either by the prohibitive cost of accurate electronic
structure theory computations or the limited accuracy of classical empirical
force fields. Machine learning techniques can help to overcome these
limitations by providing access to potential energies, forces and other
molecular properties modeled directly after an electronic structure reference
at only a fraction of the original computational cost. The present text
discusses several practical aspects of conducting machine learning driven
molecular dynamics simulations. First, we study the efficient selection of
reference data points on the basis of an active learning inspired adaptive
sampling scheme. This is followed by the analysis of a machine-learning based
model for simulating molecular dipole moments in the framework of predicting
infrared spectra via molecular dynamics simulations. Finally, we show that
machine learning models can offer valuable aid in understanding chemical
systems beyond a simple prediction of quantities.
