Machine learning determines the Mg2SiO4 P-T phase diagram
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908804994564096 |
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| author | Zhou, Siyu Liu, Daohong Zhang, Chuanyu He, Yu Wang, Xuben Zuo, Xiaopan |
| author_facet | Zhou, Siyu Liu, Daohong Zhang, Chuanyu He, Yu Wang, Xuben Zuo, Xiaopan |
| contents | Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase boundaries remain debated, largely due to experimental limitations at extreme conditions and the high computational cost of first-principles free-energy calculations. Here, a machine-learning-potential driven workflow combining non-equilibrium thermodynamic integration (NETI) and two-phase coexistence simulations is employed to enable large-scale, long-timescale molecular dynamics sampling. Within this workflow, the melting curve of forsterite is evaluated and a complete P-T phase diagram is constructed. Relative to conventional ab initio approaches, this strategy reduces computational expense while retaining thermodynamic consistency in phase-stability assessment. The workflow is applicable to efficient evaluation of phase stability and thermodynamic properties in deep-Earth silicate systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01730 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine learning determines the Mg2SiO4 P-T phase diagram Zhou, Siyu Liu, Daohong Zhang, Chuanyu He, Yu Wang, Xuben Zuo, Xiaopan Geophysics Materials Science Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase boundaries remain debated, largely due to experimental limitations at extreme conditions and the high computational cost of first-principles free-energy calculations. Here, a machine-learning-potential driven workflow combining non-equilibrium thermodynamic integration (NETI) and two-phase coexistence simulations is employed to enable large-scale, long-timescale molecular dynamics sampling. Within this workflow, the melting curve of forsterite is evaluated and a complete P-T phase diagram is constructed. Relative to conventional ab initio approaches, this strategy reduces computational expense while retaining thermodynamic consistency in phase-stability assessment. The workflow is applicable to efficient evaluation of phase stability and thermodynamic properties in deep-Earth silicate systems. |
| title | Machine learning determines the Mg2SiO4 P-T phase diagram |
| topic | Geophysics Materials Science |
| url | https://arxiv.org/abs/2602.01730 |