  Reliable uncertainty measures are required when using data based machine
learning interatomic potentials (MLIPs) for atomistic simulations. In this
work, we propose for sparse Gaussian Process Regression type MLIP a stochastic
uncertainty measure akin to the query-by-committee approach often used in
conjunction with neural network based MLIPs. The uncertainty measure is coined
\textit{"label noise"} ensemble uncertainty as it emerges from adding noise to
the energy labels in the training data. We find that this method of calculating
an ensemble uncertainty is as well calibrated as the one obtained from the
closed-form expression for the posterior variance when the sparse GPR is
treated as a projected process. Comparing the two methods, our proposed
ensemble uncertainty is, however, faster to evaluate than the closed-form
expression. Finally, we demonstrate that the proposed uncertainty measure acts
better to support a Bayesian search for optimal structure of Au$_{20}$
clusters.
