  Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate,
classical molecular dynamics simulations of large systems, beyond reach of
density functional theory (DFT). Yet, their efficiency and ability to predict
systems larger than DFT supercells are not fully explored, posing a question
regarding transferability to large-scale simulations with defects (e.g.
dislocations, cracks). Here, we apply a three-step validation approach to
body-centered-cubic iron. First, accuracy and efficiency are assessed by
optimizing ML-IAPs based on four state-of-the-art ML packages. The Pareto front
of computational speed versus testing root-mean-square-error (RMSE) is
computed. Second, benchmark properties relevant to plasticity and fracture are
evaluated. Their average relative error Q with respect to DFT is found to
correlate with RMSE. Third, transferability of ML-IAPs to dislocations and
cracks is investigated by using per-atom model uncertainty quantification. The
core structures and Peierls barriers of screw, M111 and three edge dislocations
are compared with DFT. Traction-separation curve and critical stress intensity
factor (K_Ic) are also predicted. Cleavage on the pre-existing crack plane is
found to be the zero-temperature atomistic fracture mechanism of pure
body-centered-cubic iron under mode-I loading, independent of ML package and
training database. Quantitative predictions of dislocation glide paths and KIc
can be sensitive to database, ML package, cutoff radius, and are limited by DFT
accuracy. Our results highlight the importance of validating ML-IAPs by using
indicators beyond RMSE. Moreover, significant computational speed-ups can be
achieved by using the most efficient ML-IAP package, yet the assessment of the
accuracy and transferability should be performed with care.
