Elasticity and Viscosity of hcp Iron at Earth's Inner Core Conditions From Machine Learning-Based Large-Scale Atomistic Simulations

Although considerable efforts have been made in the last years to examine the physical properties of hexagonal close-packed (hcp) iron at extreme conditions, it remains challenging to explain many geophysical observations in Earth's inner core. Here we examine the elastic and plastic behavior of hcp iron and the effects of structural defects at inner core conditions using large-scale atomistic simulations coupled with machine learning-based interatomic potential. Our results suggest that the seismic anisotropy pattern in the inner core can be ascribed to the elastic anisotropy (6%) of hcp iron. The observed low shear wave velocity is largely produced by viscous grain boundaries in iron polycrystal. We also found highly mobile and abundant vacancies in hcp iron yield a viscous strength (10(15 +/- 1)) that is consistent with the geophysical observations. Therefore, our findings highlight the role played by structural defects and lessen the demand for light elements to explain the observed seismic data.