Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.

Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulations with accuracies akin to first-principles simulations. Notwithstanding their success in accurately describing the physical properties of pristine ionic systems with multiple oxidation states, herein we show that an implementation of deep neural network potentials (DNPs) yield vacancy formation energies in MgO with a significant ∼3 eV error. In contrast, we show that moment tensor potentials can accurately describe all properties of the oxide, including vacancy formation energies. We show that the vacancy formation energy errors in DNPs correlate with the strength of ionic interactions in the system as evidenced by contrasting MgO with the less ionic systems CuO and AgO. Our findings suggest that descriptors employed in the DNP may be inadequate and cannot accurately describe vacancies in ionic systems.