  Atomistic simulations often rely on interatomic potentials to access greater
time- and length- scales than those accessible to first principles methods such
as density functional theory (DFT). However, since a parameterised potential
typically cannot reproduce the true potential energy surface of a given system,
we should expect a decrease in accuracy and increase in error in quantities of
interest calculated from simulations. Quantifying the uncertainty on the
outputs of atomistic simulations is thus an important, necessary step so that
there is confidence in results and available metrics to explore improvements in
said simulations. Here, we address this research question by forming ensembles
of Atomic Cluster Expansion (ACE) potentials, and using Conformal Prediction
with DFT training data to provide meaningful, calibrated error bars on several
quantities of interest for silicon: the bulk modulus, elastic constants,
relaxed vacancy formation energy, and the vacancy migration barrier. We
evaluate the effects on uncertainty bounds using a range of different
potentials and training sets.
