Phase equilibria and interfacial properties of selected methane + n-alkane binary mixtures

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

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 (λr) ranging from 7 to 34 and at a fixed attractive exponent (λa) 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.

Development of thermodynamically consistent machine-learning equations of state: Application to the Mie fluid

A procedure for deriving thermodynamically consistent data-driven equations of state (EoS) for fluids is presented. The method is based on fitting the Helmholtz free energy using artificial neural networks to obtain a closed-form relationship between the thermophysical properties of fluids (FE-ANN EoS). As a proof-of-concept, an FE-ANN EoS is developed for the Mie fluids, starting from a database obtained by classical molecular dynamics simulations. The FE-ANN EoS is trained using first- (pressure and internal energy) and second-order (e.g., heat capacities, Joule-Thomson coefficients) derivative data. Additional constraints ensure that the data-driven model fulfills thermodynamically consistent limits and behavior. The results for the FE-ANN EoS are shown to be as accurate as the best available analytical model while being developed in a fraction of the time. The robustness of the "digital" equation of state is exemplified by computing physical behavior it has not been trained on, for example, fluid phase equilibria. Furthermore, the model's internal consistency is successfully assessed using Brown's characteristic curves.

Molecular dynamics of liquid-liquid equilibrium and interfacial properties of aqueous solutions of methyl esters

In this work, the liquid-liquid phase equilibria and interfacial properties of methyl ester + water binary mixtures are determined at atmospheric pressure and from 278 to 358 K combining the direct coexistence technique and molecular dynamics simulations. Methyl esters are modelled using new parametrizations based on the united atom TraPPE model force field proposed recently by us [E. Feria, J. Algaba, J. M. Míguez, A. Mejía, P. Gómez-Álvarez and F. J. Blas, Phys. Chem. Chem. Phys., 2019, 22, 4974-4983] that are able to predict the vapour-liquid interfacial properties of pure methyl esters with high accuracy. In the case of water, we consider the well-known TIP4P/2005 model, the most popular rigid and non-polarizable model to describe the interfacial properties of pure water. The simulations are performed using the direct coexistence technique in the isothermal-isobaric or NP_zT ensemble in combination with molecular dynamics. We obtain density profiles, temperature-densities and temperature-composition projections of the phase diagrams, and interfacial tensions. The liquid-liquid interfacial tension is calculated from the normal and tangential components of the pressure tensor according to the mechanical virial route. We pay attention particularly to the ability of the molecular models in predicting the experimental behavior of the systems. Simulation results are able to account for the liquid-liquid phase equilibria of these binary mixtures, in good agreement with the experimental data taken from the literature. Unfortunately, experimental values for interfacial tensions are substantially overestimated by predictions from computer simulations in all cases. To our knowledge, this is the first time that the liquid-liquid phase equilibrium and interfacial properties of methyl ester + water mixtures have been predicted from computer simulations.