  The atomic cluster expansion (ACE) was proposed recently as a new class of
data-driven interatomic potentials with a formally complete basis set. Since
the development of any interatomic potential requires a careful selection of
training data and thorough validation, an automation of the construction of the
training dataset as well as an indication of a model's uncertainty are highly
desirable. In this work, we compare the performance of two approaches for
uncertainty indication of ACE models based on the D-optimality criterion and
ensemble learning. While both approaches show comparable predictions, the
extrapolation grade based on the D-optimality (MaxVol algorithm) is more
computationally efficient. In addition, the extrapolation grade indicator
enables an active exploration of new structures, opening the way to the
automated discovery of rare-event configurations. We demonstrate that active
learning is also applicable to explore local atomic environments from
large-scale MD simulations.
