  The MACE architecture represents the state of the art in the field of machine
learning force fields for a variety of in-domain, extrapolation and low-data
regime tasks. In this paper, we further evaluate MACE by fitting models for
published benchmark datasets. We show that MACE generally outperforms
alternatives for a wide range of systems from amorphous carbon, universal
materials modelling, and general small molecule organic chemistry to large
molecules and liquid water. We demonstrate the capabilities of the model on
tasks ranging from constrained geometry optimisation to molecular dynamics
simulations and find excellent performance across all tested domains. We show
that MACE is very data efficient, and can reproduce experimental molecular
vibrational spectra when trained on as few as 50 randomly selected reference
configurations. We further demonstrate that the strictly local atom-centered
model is sufficient for such tasks even in the case of large molecules and
weakly interacting molecular assemblies.
