摘要
氮化硅(β-Si_(3)N_(4))是当下最具应用前景的热透波材料,其基础物性高温介电函数的精准测量对加快氮化硅基热透波材料的设计,解决高超声速飞行器“黑障”问题具有重要意义.本文以第一性原理数据为基本输入,基于深度神经网络训练得到深度学习势,然后运用深度学习分子动力学方法计算了氮化硅高温微波介电函数.与传统经验势相比,深度学习势的计算结果与实验结果在数量级上保持一致;同时发现,深度学习分子动力学在计算速度方面表现优异.此外,建立了氮化硅弛豫时间温度依变性的物理模型,揭示了弛豫时间温度依变性规律.本研究通过实现大规模高精度的分子动力学模拟计算了氮化硅高温微波介电函数,为推动氮化硅基材料在高温热透波领域的应用提供了基础数据支撑.
Silicon nitride(β-Si_(3)N_(4))is a most promising thermal wave-transparent material.The accurate measurement of its high-temperature dielectric function is essential to solving the“black barrier”problem of hypersonic vehicles and accelerating the design of silicon nitride-based thermal wave-transparent materials.Direct experimental measurement at high temperature is a difficult job and the accuracy of classical molecular dynamics(CMD)simulations suffers the choice of empirical potential.In this work,we build a β-Si_(3)N_(4) model on a nanoscale,train the deep learning potential(DLP)by using first-principles data,and apply the deep potential molecular dynamics(DPMD)to simulate the polarization relaxation process.The predicted energy and force by DLP are excellently consistent with first-principles calculations,which proves the high accuracy of DLP.The RMSEs for β-Si_(3)N_(4) are quite low(0.00550 meV/atom for energy and 7.800 meV/Åfor force).According to the Cole-Cole formula,the microwave dielectric function in the temperature range of 300-1000 K is calculated by using the deep learning molecular dynamics method.Compared with the empirical potential,the computational results of the DLP are consistent with the experimental results in the sense of order of magnitude.It is also found that the DPMD performs well in terms of computational speed.In addition,a mathematical model of the temperature dependence of the relaxation time is established to reveal the pattern of relaxation time varying with temperature.The high-temperature microwave dielectric function of silicon nitride is calculated by implementing large-scale and high-precision molecular dynamics simulations.It provides fundamental data for promoting the application of silicon nitride in high-temperature thermal transmission.
作者
李志强
谭晓瑜
段忻磊
张敬义
杨家跃
Li Zhi-Qiang;Tan Xiao-Yu;Duan Xin-Lei;Zhang Jing-Yi;Yang Jia-Yue(Optics&Thermal Radiation Research Center,Institute of Frontier and Interdisciplinary Science,Shandong University,Qingdao 266237,China;School of Energy and Power Engineering,Shandong University,Jinan 250061,China;Science and Technology on Advanced Functional Composite Laboratory,Aerospace Research Institute of Materials&Processing Technology,Beijing 100076,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2022年第24期399-407,共9页
Acta Physica Sinica
关键词
热透波材料
介电函数
高温
深度学习势
thermal wave-transparent material
dielectric function
high temperature
deep learning potential
