  Machine-learning (ML) interatomic potentials (IPs) trained on
first-principles datasets are becoming increasingly popular since they promise
to treat larger system sizes and longer time scales, compared to the {\em ab
initio} techniques producing the training data. Estimating the accuracy of
MLIPs and reliably detecting when predictions become inaccurate is key for
enabling their unfailing usage. In this paper, we explore this aspect for a
specific class of MLIPs, the equivariant-neural-network (ENN) IPs using the
ensemble technique for quantifying their prediction uncertainties. We
critically examine the robustness of uncertainties when the ENN ensemble IP
(ENNE-IP) is applied to the realistic and physically relevant scenario of
predicting local-minima structures in the configurational space. The ENNE-IP is
trained on data for liquid silicon, created by density-functional theory (DFT)
with the generalized gradient approximation (GGA) for the exchange-correlation
functional. Then, the ensemble-derived uncertainties are compared with the
actual errors (comparing the results of the ENNE-IP with those of the
underlying DFT-GGA theory) for various test sets, including liquid silicon at
different temperatures and out-of-training-domain data such as solid phases
with and without point defects as well as surfaces. Our study reveals that the
predicted uncertainties are generally overconfident and hold little
quantitative predictive power for the actual errors.
