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Melting temperature of iron under the Earth’s inner core condition from deep machine learning
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作者 Fulun Wu Shunqing Wu +3 位作者 Cai-Zhuang Wang Kai-Ming Ho Renata M.Wentzcovitch Yang Sun 《Geoscience Frontiers》 SCIE CAS CSCD 2024年第6期485-493,共9页
Constraining the melting temperature of iron under Earth’s inner core conditions is crucial for understanding core dynamics and planetary evolution.Here,we develop a deep potential(DP)model for iron that explicitly i... Constraining the melting temperature of iron under Earth’s inner core conditions is crucial for understanding core dynamics and planetary evolution.Here,we develop a deep potential(DP)model for iron that explicitly incorporates electronic entropy contributions governing thermodynamics under Earth’s core conditions.Extensive benchmarking demonstrates the DP’s high fidelity across relevant iron phases and extreme pressure and temperature conditions.Through thermodynamic integration and direct solid–liquid coexistence simulations,the DP predicts melting temperatures for iron at the inner core boundary,consistent with previous ab initio results.This resolves the previous discrepancy of iron’s melting temperature at ICB between the DP model and ab initio calculation and suggests the crucial contribution of electronic entropy.Our work provides insights into machine learning melting behavior of iron under core conditions and provides the basis for future development of binary or ternary DP models for iron and other elements in the core. 展开更多
关键词 Inner core boundary Melting temperature Machine learning Solid-liquid coexistence Free energy calculation Molecular dynamics simulation
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