Melting of MgSiO3 determined by machine learning potentials
Melting of MgSiO3 determined by machine learning potentials
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DOI：
10.1103/physrevb.107.064103
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发表时间：
2023-02-13
影响因子：
3.7
通讯作者：
Stixrude, Lars
中科院分区：
文献类型：
--
作者：
Deng, Jie;Niu, Haiyang;Stixrude, Lars
在行星的深岩部分熔化对于理解这些天体的热演化以及在其底层金属核心中可能产生的磁场非常重要。但是，硅酸盐的熔化温度在超级地球系外行星（银河系中最丰富的行星类型）的压力下受到的限制很小。在这里，我们提出了一种迭代学习方案，该方案结合了增强的采样、特征选择和深度学习，并开发了一种在宽压力-温度范围内有效的从头算质量的统一机器学习潜力，以确定MgSiO 3的熔化温度。高压后钙钛矿相的熔化温度，对超级地球很重要，随着压力的增加，比稳定在地幔底部的低压钙钛矿相的熔化温度增加得更快。在我们研究的最高压力下，液体的体积接近固相的体积。我们计算的三相点约束的克拉珀龙斜率的钙钛矿后钙钛矿过渡，我们比较地震反射率在地球的地幔底部的观测校准地球的核心热通量。
Melting in the deep rocky portions of planets is important for understanding the thermal evolution of these bodies and the possible generation of magnetic fields in their underlying metallic cores. But the melting tempera-ture of silicates is poorly constrained at the pressures expected in super-Earth exoplanets, the most abundant type of planets in the galaxy. Here, we propose an iterative learning scheme that combines enhanced sampling, feature selection, and deep learning, and develop a unified machine learning potential of ab initio quality valid over a wide pressure-temperature range to determine the melting temperature of MgSiO3. The melting temperature of the high-pressure, post-perovskite phase, important for super-Earths, increases more rapidly with increasing pressure than that of the lower pressure perovskite phase, stable at the base of Earth's mantle. The volume of the liquid closely approaches that of the solid phases at the highest pressure of our study. Our computed triple point constrains the Clapeyron slope of the perovskite to post-perovskite transition, which we compare with observations of seismic reflectivity at the base of Earth's mantle to calibrate Earth's core heat flux.