Silica $\left({\mathrm{SiO}}_2\right)$ 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 ${\mathrm{SiO}}_2$ that faithfully represent the strongly constrained and appropriately normed (SCAN) and Perdew-Burke-Ernzerhof revised for solids (PBEsol) exchange-correlation functionals over a wide temperature (1000–10 000 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 ${\mathrm{SiO}}_2$ 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 those of 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.

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• Earth's interior
• Liquid-solid phase transition
• Phase transitions
• Extrasolar planets
• Density functional theory
• First-principles calculations
• Machine learning