Simulation and Data-Driven Modeling of the Transport Properties of the Mie Fluid

Abstract

This work reports the computation and modeling of the self-diffusivity (D*), shear viscosity (η*), and thermal conductivity (κ*) of the Mie fluid. The transport properties were computed using equilibrium molecular dynamics simulations for the Mie fluid with repulsive exponents (λ r ) ranging from 7 to 34 and at a fixed attractive exponent (λ a ) of 6 over the whole fluid density (ρ*) range and over a wide temperature (T*) range. The computed database consists of 17,212, 14,288, and 13,099 data points for self-diffusivity, shear viscosity, and thermal conductivity, respectively. The database is successfully validated against published simulation data. The above-mentioned transport properties are correlated using artificial neural networks (ANNs). Two modeling approaches were tested: a semiempirical formulation based on entropy scaling and an empirical formulation based on density and temperature as input variables. For the former, it was found that a unique formulation based on entropy scaling does not yield satisfactory results over the entire density range due to a divergent and incorrect scaling of the transport properties at low densities. For the latter empirical modeling approach, it was found that regularizing the data, e.g., modeling ρ*D* instead of D*, ln η* instead of η*, and ln κ* instead of κ*, as well as using the inverse of the temperature as an input feature, helps to ease the interpolation efforts of the artificial neural networks. The trained ANNs can model seen and unseen data over a wide range of density and temperature. Ultimately, the ANNs can be used alongside equations of state to regress effective force field parameters from volumetric and transport data.

On the continuous modeling of fluid and solid states

“…The database quality is checked by comparison of first-order properties in different ensembles. Moreover, possible outliers are detected and detected using the method explained in our previous work [45]. This methodology obtains the "outlierness" scores from the LocalOutlierFactor method implemented in Scikit-Learn [46].…”

On the continuous modeling of fluid and solid states

“…The database quality is checked by comparison of first-order properties in different ensembles. Moreover, possible outliers are detected and detected using the method explained in our previous work [44]. This methodology obtains the "outlierness" scores from the LocalOutlierFactor method implemented in Scikit-Learn [45].…”

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC B , the papers address ML applications in protein engineering, materials design, novel methods, and properties of liquids. Several contributions specifically address the novel uses of ML as a methodological improvement or innovation. − Another common thread among the papers is the use of ML to study issues related to molecular conformers or other structure–function relationships. − A number of articles demonstrate that ML has continued to grow as an important tool for the study of complex liquids and their properties. − Finally, papers in JPC B also show recent advances in the application of ML to the study of peptides, proteins, and their properties. − …”