The ability to predict transport properties of fluids, such as the self-diffusion coefficient and viscosity, has been an ongoing effort in the field of molecular modeling. While there are theoretical approaches to predict the transport properties of simple systems, they are typically applied in the dilute gas regime and are not directly applicable to more complex systems. Other attempts to predict transport properties are performed by fitting available experimental or molecular simulation data to empirical or semi-empirical correlations. Recently, there have been attempts to improve the accuracy of these fittings through the use of Machine-Learning (ML) methods. In this work, the application of ML algorithms to represent the transport properties of systems comprising spherical particles interacting via the Mie potential is investigated. To this end, the self-diffusion coefficient and shear viscosity of 54 potentials are obtained at different regions of the fluid-phase diagram. This data set is used together with three ML algorithms, namely, k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Symbolic Regression (SR), to find correlations between the parameters of each potential and the transport properties at different densities and temperatures. It is shown that ANN and KNN perform to a similar extent, followed by SR, which exhibits larger deviations. Finally, the application of the three ML models to predict the self-diffusion coefficient of small molecular systems, such as krypton, methane, and carbon dioxide, is demonstrated using molecular parameters derived from the so-called SAFT-VR Mie equation of state [T. Lafitte et al. J. Chem. Phys. 139, 154504 (2013)] and available experimental vapor–liquid coexistence data.
Application of machine-learning algorithms to predict the transport properties of Mie fluids
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Note: This paper is part of the JCP Special Topic on Machine Learning Hits Molecular Simulations.
Justinas Šlepavičius, Alessandro Patti, James L. McDonagh, Carlos Avendaño; Application of machine-learning algorithms to predict the transport properties of Mie fluids. J. Chem. Phys. 14 July 2023; 159 (2): 024127. https://doi.org/10.1063/5.0151123
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