Abstract
Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of
popularity owing to their accuracy in comparison to conventional empirical potentials. However,
the transferability of MLIP to systems outside the training-set poses a significant challenge. Here,
we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii)
Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello
descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We obtained
good agreement between each MLIP models and density functional theory reference results for
physical properties including bulk lattice parameters, surface energies, and vibrational density of
states. However, the NNP model performed poorly when compared to the other two linear models
for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases,
which are systems outside the training-set. While providing less accurate prediction for solid Zinc
Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale
systems and liquid phases