Testing and validation of a self-diffusion coefficient model based on molecular dynamics simulations

doi:10.1016/j.cjche.2021.04.036

Abstract

Abstract: In our previous work, we endowed a new physical meaning of self-diffusion coefficient in Fick's law, which proposed that the diffusion coefficient can be described as the product of the characteristic length and the diffusion velocity. To testify this simple theory, in this work, we further investigated the underlying mechanism of the characteristic length and the diffusion velocity at the molecular level. After a complete dynamic run, the statistical average diffusion velocity and the characteristic length of molecules can be obtained by scripts, and subsequently the diffusion coefficient was determined by our proposed theory. The diffusion processes in 35 systems with a wide range of pressure and concentration variations were simulated using this model. From the simulated results, diffusion coefficients from our new model matched well with the experimental results from literatures. The total average relative deviation of predicted values with respect to the experimental results is 8.18%, indicating that the novel model is objective and rational. Compared with the traditional MSD-t model, this novel diffusion coefficient model provides more reliable results, and the theory is simple and straightforward in concept. Additionally, the effect of gas pressure and liquid concentration on the diffusion behavior were discussed, and the microscopic diffusion mechanism was elucidated through the distribution of diffusion velocity and the characteristic length analysis. Moreover, we suggested new distribution functions, providing more reliable data theoretical foundations for the future research about the diffusion coefficient.

Key words: Diffusion, Molecular simulation, Parameter identification, Characteristic length, Diffusion velocity

摘要： In our previous work, we endowed a new physical meaning of self-diffusion coefficient in Fick's law, which proposed that the diffusion coefficient can be described as the product of the characteristic length and the diffusion velocity. To testify this simple theory, in this work, we further investigated the underlying mechanism of the characteristic length and the diffusion velocity at the molecular level. After a complete dynamic run, the statistical average diffusion velocity and the characteristic length of molecules can be obtained by scripts, and subsequently the diffusion coefficient was determined by our proposed theory. The diffusion processes in 35 systems with a wide range of pressure and concentration variations were simulated using this model. From the simulated results, diffusion coefficients from our new model matched well with the experimental results from literatures. The total average relative deviation of predicted values with respect to the experimental results is 8.18%, indicating that the novel model is objective and rational. Compared with the traditional MSD-t model, this novel diffusion coefficient model provides more reliable results, and the theory is simple and straightforward in concept. Additionally, the effect of gas pressure and liquid concentration on the diffusion behavior were discussed, and the microscopic diffusion mechanism was elucidated through the distribution of diffusion velocity and the characteristic length analysis. Moreover, we suggested new distribution functions, providing more reliable data theoretical foundations for the future research about the diffusion coefficient.

关键词: Diffusion, Molecular simulation, Parameter identification, Characteristic length, Diffusion velocity

Xia Chen, Yan Wang, Lianying Wu, Weitao Zhang, Yangdong Hu. Testing and validation of a self-diffusion coefficient model based on molecular dynamics simulations[J]. Chinese Journal of Chemical Engineering, 2021, 36(8): 138-145.

Xia Chen, Yan Wang, Lianying Wu, Weitao Zhang, Yangdong Hu. Testing and validation of a self-diffusion coefficient model based on molecular dynamics simulations[J]. 中国化学工程学报, 2021, 36(8): 138-145.

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