Silica (SiO2) is fundamental to both industrial technology and planetary science, yet the phase relations of its high-pressure polymorphs remain poorly constrained. Here, we develop two machine learning potentials (MLPs) for SiO2 that faithfully represent the SCAN and PBEsol exchange-correlation functionals over a wide temperature (1000-10000 K) and pressure (100-400 GPa) range using deep neural networks. With large-scale two-phase simulations powered by these potentials, we determine the melting curves of seifertite and pyrite-type SiO2 and infer the solid-solid phase boundary between these two phases. The SCAN functional, which captures intermediate-range van der Waals interactions, reproduces structural and thermodynamic properties with high fidelity, predicting melting temperatures 6-10 % higher and a seifertite to pyrite-type transition pressure 22 % higher than the PBEsol. The strongly negative Clapeyron slope (-6.1 MPa/K) of this transition suggests that mantle convection could be highly layered in super-Earth exoplanets, potentially affecting their long-term thermal evolution and habitability.
@article{arxiv.2510.13119,
title = {Melting phase relation of seifertite and pyrite-type SiO2 determined by machine learning potentials},
author = {Doyoon Park and Xin Deng and Jie Deng},
journal= {arXiv preprint arXiv:2510.13119},
year = {2025}
}