In recent years, machine learning potentials have gained importance as data-driven energy and force predictors for atomistic simulations, achieving an accuracy close to ab-initio results while offering a considerable speedup. The underlying model architectures have improved from neural networks built on simple invariant encodings of the atom environment to elaborate equivariant, possibly multi-body interactions based on graph neural networks or graph transformers allowing both more precise and more data-efficient models.