Thermoelastic properties of bridgmanite using deep-potential molecular dynamics

The high-pressure Pbnm-perovskite polymorph of ${\mathrm{MgSiO}}_{3}$, i.e., bridgmanite (Bm), plays a crucial role in the Earth's lower mantle. It is likely responsible for \ensuremath{\sim}75 vol. % of this region and its properties dominate the properties of this region, especially its elastic properties that are challenging to measure at ambient conditions. This study combines deep-learning potential (DP) with density-functional theory (DFT) to investigate the structural and elastic properties of Bm under lower-mantle conditions. To simulate this system, we developed a series of potentials capable of faithfully reproducing DFT calculations using different functionals, i.e., local density approximation (LDA), Perdew-Burke-Ernzerhof parametrization (PBE), revised PBE for solids (PBEsol), and strongly constrained and appropriately normed (SCAN) meta--generalized-gradient approximation functionals. Our predictions with DP-SCAN exhibit a remarkable agreement with experimental measurements of high-temperature equations of states and elastic properties and highlight its superior performance, closely followed by DP-LDA in accurately predicting. This hybrid computational approach offers a solution to the accuracy-efficiency dilemma in obtaining precise elastic properties at high pressure and temperature conditions for minerals like Bm, opening a way to study the Earth material's thermodynamic properties and related phenomena.