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- W4320486530 abstract "The melting temperature of MgSiO${}_{3}$ is crucial in controlling the interior structures and dynamics of Earth and super-Earths. Here, the authors propose an iterative learning scheme that combines enhanced sampling, feature selection, and deep learning, and develop a unified machine learning potential of $aphantom{rule{0}{0ex}}b$ $iphantom{rule{0}{0ex}}nphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}tphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}o$ quality. This is valid over a wide pressure-temperature range to determine the melting temperature of MgSiO${}_{3}$. Modeling based on these results shows that heat flux from the core to the mantle is favorable of generating magnetic fields." @default.
- W4320486530 created "2023-02-14" @default.
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- W4320486530 date "2023-02-13" @default.
- W4320486530 modified "2023-10-17" @default.
- W4320486530 title "Melting of <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML><mml:mrow><mml:mi>MgSi</mml:mi><mml:msub><mml:mi mathvariant=normal>O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> determined by machine learning potentials" @default.
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- W4320486530 doi "https://doi.org/10.1103/physrevb.107.064103" @default.
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