Li Hongfei, Zhu Yuanxu, Chu MengFan, Dong Haikuan, Zhang Guohua
Department of Physics, University of Science and Technology Beijing, Beijing 100083, People's Republic of China.
College of Miami, Henan University, Kaifeng 475004, People's Republic of China.
J Phys Condens Matter. 2024 May 30;36(34). doi: 10.1088/1361-648X/ad44f9.
The computation of thermal conductivity for finite nanoparticulate systems, particularly those of irregular shapes, poses significant challenges. The nonequilibrium molecular dynamics (NEMD) methods has been extensively utilized in numerous prior studies for the computation of thermal conductivity of nanoparticles. One of our recent works (Dong2021B035417) proposed that equilibrium molecular dynamics (EMD) methods can be used for the simulation of thermal conductivity of finite-scale systems and demonstrated their equivalence to NEMD methods. In this study, we investigated the application of the (EMD) approach for the computation of thermal conductivity in zero-dimensional nanoparticles. In our initial step, we merged both methodologies to substantiate the equivalence in thermal conductivity calculation for cube and cylinder nanoparticles. After filtering the data, we confirmed the usefulness of EMD for evaluating the thermal conductivity of zero-dimensional materials. The NEMD method faces challenges in accurately predicting thermal conductivity in nanoparticle systems with a varying cross-sectional area along the transport direction, whereas EMD methods can be utilized to estimate thermal conductivity when the volume is known. In a subsequent study, we used the state-of-the-art machine learning potential to calculate the thermal conductivity of spherical nanoparticles and compared the results with those obtained using the classical Tersoff potential. Ultimately, we predicted the thermal conductivity of nanoparticles with various geometries in all directions. Our findings collectively demonstrate the simplicity and effectiveness of employing EMD methods for calculating thermal conductivity in nanoparticle systems, thereby opening up new avenues for investigating thermal transport properties in particle systems as well as nanopders.
对于有限的纳米颗粒系统，特别是那些形状不规则的系统，热导率的计算面临着重大挑战。非平衡分子动力学（NEMD）方法在许多先前的研究中已被广泛用于计算纳米颗粒的热导率。我们最近的一项工作（Dong2021B035417）提出，平衡分子动力学（EMD）方法可用于有限尺度系统热导率的模拟，并证明了它们与NEMD方法的等效性。在本研究中，我们研究了（EMD）方法在零维纳米颗粒热导率计算中的应用。在我们的初始步骤中，我们合并了这两种方法，以证实立方体和圆柱体纳米颗粒在热导率计算中的等效性。在对数据进行过滤后，我们证实了EMD在评估零维材料热导率方面的有用性。NEMD方法在准确预测沿传输方向具有变化横截面积的纳米颗粒系统中的热导率方面面临挑战，而当体积已知时，EMD方法可用于估计热导率。在随后的一项研究中，我们使用了最先进的机器学习势来计算球形纳米颗粒的热导率，并将结果与使用经典Tersoff势获得的结果进行了比较。最终，我们预测了各种几何形状的纳米颗粒在各个方向上的热导率。我们的研究结果共同证明了采用EMD方法计算纳米颗粒系统热导率的简单性和有效性，从而为研究颗粒系统以及纳米粉体中的热传输特性开辟了新途径。
