高压物理学报2026，Vol.40Issue(1)：77-89,13.DOI:10.11858/gywlxb.20251221
基于神经网络势函数计算地球内核条件下的铁-硫合金黏度
Viscosity of Iron-Sulfur Alloy under the Conditions of the Earth Inner Core Calculated Based on the Neural Network Potential
摘要
Abstract
The density of the Earth's inner core is lower than that of pure iron,indicating the presence of light elements.Among the candidate elements,carbon,hydrogen,oxygen,sulfur,and silicon are considered the most likely.Viscosity is a key physical property controlling the dynamics and evolutionary history of the inner core,and it has significant implications for the origin of seismic anisotropy.Previous studies have investigated the viscosity of pure iron in its hexagonal close-packed(HCP)and body-centered cubic(BCC)phases under inner-core conditions through computational simulations.However,the influence of light elements on the viscosity of the inner core remains insufficiently constrained.In this study,we constructed a neural network potential(NNP)for Fe-S alloy under inner-core conditions and employed it to perform large-scale molecular dynamics simulations.We systematically examined the impact of vacancy concentrations as low as 0.01%on the ionic transport properties of Fe-S alloy.Based on the self-diffusion coefficients of Fe in the lattice,we further explored the creep mechanisms and viscosity of Fe-S alloy under core conditions.Our results indicate that dislocation creep dominates the rheological behavior,yielding viscosities of 1×1014-2×1016 Pa·s,consistent with constraints from free-core nutation and seismic observations.关键词
神经网络势函数/地球内核/自扩散系数/黏度/分子动力学Key words
neural network potential/Earth's inner core/self-diffusion coefficient/viscosity/molecular dynamics分类
数理科学引用本文复制引用
XU Yunfan,HE Yu,ZHANG Wei,LI Heping..基于神经网络势函数计算地球内核条件下的铁-硫合金黏度[J].高压物理学报,2026,40(1):77-89,13.基金项目
国家自然科学基金(42350002,42074104) （42350002,42074104）
中国科学院青年交叉团队项目(JCTD-2022-1) （JCTD-2022-1）
中国科学院青年创新促进会项目(2020394) （2020394）
贵州省2020年科技专项补助项目(NGZ2020SIG) （NGZ2020SIG）
