  We introduce ACEpotentials.jl, a Julia-language software package that
constructs interatomic potentials from quantum mechanical reference data using
the Atomic Cluster Expansion (Drautz, 2019). As the latter provides a complete
description of atomic environments, including invariance to overall translation
and rotation as well as permutation of like atoms, the resulting potentials are
systematically improvable and data efficient. Furthermore, the descriptor's
expressiveness enables use of a linear model, facilitating rapid evaluation and
straightforward application of Bayesian techniques for active learning. We
summarize the capabilities of ACEpotentials.jl and demonstrate its strengths
(simplicity, interpretability, robustness, performance) on a selection of
prototypical atomistic modelling workflows.
