大规模、量子精度的分子动力学模拟:以极端条件液态铁为例  

Large scale and quantum accurate molecular dynamics simulation:Liquid iron under extreme condition

作　　者：曾启昱 陈博 康冬冬[1,2] 戴佳钰 Zeng Qi-Yu;Chen Bo;Kang Dong-Dong;Dai Jia-Yu(College of Science,National University of Defense Technology,Changsha 410073,China;Hunan Key Laboratory of Extreme Matter and Applications,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学理学院,长沙410073 [2]国防科技大学,极端条件物理及应用湖南省重点实验室,长沙410073

出　　处：《物理学报》2023年第18期129-136,共8页Acta Physica Sinica

基　　金：NSAF联合基金(批准号:U1830206);国家自然科学基金(批准号:11874424,12104507);湖南省科技创新领军人才项目(批准号:2021RC4026)资助的课题。

摘　　要：液态铁作为类地行星内核的主要组成成分,其在高温高压条件下的热力学、输运及动力学性质研究,对理解行星演化有着重要意义.极端条件物态物性在实验条件下产生困难且诊断手段有限,而理论模拟在动力学、输运性质计算方面面临着规模、精度的双重要求,极大限制了这方面的有效进展.本文结合深度学习技术,通过神经网络构造液态铁的高维相互作用势能面,在保证第一性原理计算精度的前提下,将计算规模从数百原子扩展到数十万原子体系.研究了从常压到核幔边界条件下液态铁的动力学及输运性质,并与X射线衍射、非弹性X射线散射实验对比,二者的一致性指出,深度学习技术与分子模拟的结合为我们高通量研究极端条件下真实体系的物态物性及动力学提供了有效手段.Liquid iron is the major component of planetary cores.Its structure and dynamics under high pressure and temperature is of great significance in studying geophysics and planetary science.However,for experimental techniques,it is still difficult to generate and probe such a state of matter under extreme conditions,while for theoretical method like molecular dynamics simulation,the reliable estimation of dynamic properties requires both large simulation size and ab initio accuracy,resulting in unaffordable computational costs for traditional method.Owing to the technical limitation,the understanding of such matters remains limited.In this work,combining molecular dynamics simulation,we establish a neural network potential energy surface model to study the static and dynamic properties of liquid iron at its extreme thermodynamic state close to core-mantle boundary.The implementation of deep neural network extends the simulation scales from one hundred atoms to millions of atoms within quantum accuracy.The estimated static and dynamic structure factor show good consistency with all available X-ray diffraction and inelastic X-ray scattering experimental observations,while the empirical potential based on embedding-atom-method fails to give a unified description of liquid iron across a wide range of thermodynamic conditions.We also demonstrate that the transport property like diffusion coefficient exhibits a strong size effect,which requires more than at least ten thousands of atoms to give a converged value.Our results show that the combination of deep learning technology and molecular modelling provides a way to describe matter realistically under extreme conditions.

关 键 词：分子动力学 神经网络 极端条件 动力学性质 

分 类 号：TG141[一般工业技术—材料科学与工程] O561[金属学及工艺—金属材料]