  Recent advances in machine-learning interatomic potentials have enabled the
efficient modeling of complex atomistic systems with an accuracy that is
comparable to that of conventional quantum mechanics based methods. At the same
time, the construction of new machine-learning potentials can seem a daunting
task, as it involves data-science techniques that are not yet common in
chemistry and materials science. Here, we provide a tutorial-style overview of
strategies and best practices for the construction of artificial neural network
(ANN) potentials. We illustrate the most important aspects of (i) data
collection, (ii) model selection, (iii) training and validation, and (iv)
testing and refinement of ANN potentials on the basis of practical examples.
Current research in the areas of active learning and delta learning are also
discussed in the context of ANN potentials. This tutorial review aims at
equipping computational chemists and materials scientists with the required
background knowledge for ANN potential construction and application, with the
intention to accelerate the adoption of the method, so that it can facilitate
exciting research that would otherwise be challenging with conventional
strategies.
