Melting temperature of iron under the Earth’s inner core condition from deep machine learning  

作　　者：Fulun Wu Shunqing Wu Cai-Zhuang Wang Kai-Ming Ho Renata M.Wentzcovitch Yang Sun 

机构地区：[1]Department of Physics,Xiamen University,Xiamen 361005,China [2]Department of Physics,Iowa State University,Ames,IA 50011,USA [3]Department of Applied Physics and Applied Mathematics,Columbia University,New York,NY 10027,USA [4]Department of Earth and Environmental Sciences,Columbia University,New York,NY 10027,USA [5]Lamont–Doherty Earth Observatory,Columbia University,Palisades,NY 10964,USA

出　　处：《Geoscience Frontiers》2024年第6期485-493,共9页地学前缘（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.42374108 and 12374015);Y.S.acknowledges support from Fundamental Research Funds for the Central Universities(Grant No.20720230014);R.M.W.acknowledges support from NSF(Grant Nos.EAR-2000850 and EAR-1918126);K.M.H.acknowledges support from NSF(Grant No.EAR-1918134);Shaorong Fang and Tianfu Wu from the Information and Network Center of Xiamen University are acknowledged for their help with Graphics Processing Unit(GPU)computing.We acknowledge the supercomputing time supported by the Opening Project of the Joint Laboratory for Planetary Science and Supercomputing(Grant No.CSYYGS-QT-2024-15),Research Center for Planetary Science,and the National Supercomputing Center in Chengdu.

摘　　要：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.

关 键 词：Inner core boundary Melting temperature Machine learning Solid-liquid coexistence Free energy calculation Molecular dynamics simulation 

分 类 号：P631.3[天文地球—地质矿产勘探]