arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2404.17852physics.geo-phcond-mat.mtrl-sci
收藏

基于深度机器学习的地球内核条件下铁的熔化温度

Melting Temperature of Iron Under the Earth's Inner Core Condition from Deep Machine Learning

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

AI总结:

本研究开发包含电子熵贡献的铁深度势能模型,通过热力学积分与固液共存模拟预测内核边界熔化温度,解决了与ab initio计算的差异,为后续多元素势能模型奠定基础。

摘要:

约束地球内核条件下铁的熔化温度对于理解核动力学和行星演化至关重要。在此,我们为铁开发了一个深度势能(DP)模型,该模型明确包含了控制地球内核条件下热力学的电子熵贡献。广泛的基准测试表明,DP在相关的铁相以及极端压力和温度条件下具有高保真度。通过热力学积分和直接的固液共存模拟,DP预测了铁在内核边界(ICB)处的熔化温度,与之前的ab initio结果一致。这解决了之前DP模型与ab initio计算之间关于铁在ICB处熔化温度的差异,并表明了电子熵的关键贡献。我们的工作为机器学习研究内核条件下铁的熔化行为提供了见解,并为未来开发核中铁及其他元素的二元或三元DP模型奠定了基础。

英文摘要:

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 \textit{ab initio} results. This resolves the previous discrepancy of iron's melting temperature at ICB between the DP model and \textit{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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑