**Application of machine-learning algorithms to predict the transport
properties of Mie fluids**

J Slepavicius and A Patti and JL McDonagh and C Avendano, JOURNAL OF CHEMICAL PHYSICS, 159, 024127 (2023).

DOI: 10.1063/5.0151123

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.

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