Ferroelasticity, shear modulus softening, and the tetragonal-cubic transition in davemaoite

TL;DR

This study uses machine learning and molecular dynamics to explore the elastic properties and ferroelastic behavior of davemaoite, revealing temperature-induced shear softening and providing benchmarks for its phase transition in Earth's mantle.

Contribution

It introduces a machine-learning-based approach to accurately model davemaoite's elastic properties and ferroelastic transition, including the first simulation of its ferroelastic hysteresis loop.

Findings

Shear modulus softening occurs near the phase transition.

Ferroelastic hysteresis loop simulated for the first time.

Softening region does not reach typical slab geotherm conditions.

Abstract

Davemaoite (Dm), the cubic phase of CaSiO3-perovskite (CaPv), is a major component of the Earth's lower mantle. Understanding its elastic behavior, including its dissolution in bridgmanite (MgSiO3-perovskite), is crucial for interpreting lower mantle seismology. Using machine-learning interatomic potentials and molecular dynamics, we investigate CaPv's elastic properties across the tetragonal-cubic transition. Our equations of state align well with experimental data at 300 K and 2,000 K, demonstrating the predictive accuracy of our trained potential. We simulate the ferroelastic hysteresis loop in tetragonal CaPv, which has yet to be investigated experimentally. We also identify a significant temperature-induced shear modulus softening near the phase transition, characteristic of ferroelastic-paraelastic transitions. Unlike previous elasticity studies, our softening region does not extend to slab geotherm conditions. We suggest that ab initio-quality computations provide a robust benchmark for shear elastic softening associated with ferroelasticity, a challenging property to measure in these materials.