Melting phase relation of seifertite and pyrite-type SiO2 determined by machine learning potentials
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917015272292352 |
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| author | Park, Doyoon Deng, Xin Deng, Jie |
| author_facet | Park, Doyoon Deng, Xin Deng, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Melting phase relation of seifertite and pyrite-type SiO2 determined by machine learning potentials Park, Doyoon Deng, Xin Deng, Jie Materials Science Geophysics 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. |
| title | Melting phase relation of seifertite and pyrite-type SiO2 determined by machine learning potentials |
| topic | Materials Science Geophysics |
| url | https://arxiv.org/abs/2510.13119 |