  MgSiO_3-perovskite (MgPv) plays a crucial role in the Earth's lower mantle.
This study combines deep-learning potential (DP) with density functional theory
(DFT) to investigate the structural and elastic properties of MgPv under lower
mantle conditions. To simulate complex systems, we developed a series of
potentials capable of faithfully reproducing DFT calculations using different
functionals, such as LDA, PBE, PBEsol, and SCAN meta-GGA functionals. The
obtained predictions exhibit remarkable reliability and consistency, closely
resembling experimental measurements. Our results highlight the superior
performance of the DP-SCAN and DP-LDA in accurately predicting high-temperature
equations of states and elastic properties. 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 MgPv, which opens a new way to study the Earth's interior state and
related processes.
