  The introduction of modern Machine Learning Potentials (MLP) has led to a
paradigm change in the development of potential energy surfaces for atomistic
simulations. By providing efficient access to energies and forces, they allow
to perform large-scale simulations of extended systems, which are not directly
accessible by demanding first-principles methods. In these simulations, MLPs
can reach the accuracy of electronic structure calculations provided that they
have been properly trained and validated using a suitable set of reference
data. Due to their highly flexible functional form the construction of MLPs has
to be done with great care. In this tutorial, we describe the necessary key
steps for training reliable MLPs, from data generation via training to final
validation. The procedure, which is illustrated for the example of a
high-dimensional neural network potential, is general and applicable to many
types of MLPs.
