(482a) Partial Molar Properties from Molecular Simulation Using Multiple Linear Regression

Authors: 
Josephson, T. R., University of Minnesota
Siepmann, J. I., University of Minnesota
Partial molar volumes, energies, and enthalpies can be computed from molecular simulations in any open ensemble through a simple post-processing procedure that leverages fluctuations in composition, total volume, total energy, and pressure of a simulation box.

By recording the instantaneous box volume V and number of molecules of each of n species for M frames, a large M x n matrix is constructed for the composition, as well as an M x 1 vector of volumes. The 1 x n vector of partial molar volumes can then be solved simultaneously using multiple linear regression. A similar construction permits calculation of partial molar energies and enthalpies using M instantaneous measurements of the total energy and pressure of the system. Consequently, simulations need not be initiated with partial molar properties in mind; this method does not require accounting of intermolecular interactions or configurations, and imposes no restrictions on Monte Carlo moves or ensemble, so long as molecule populations fluctuate.

The method is demonstrated on two systems in the NpT-Gibbs and NVT-Gibbs ensembles: a relatively incompressible mixture of ethanol, dodecane, hexane, and water at liquid-liquid equilibrium [1] and a highly compressible natural gas condensate of methane, butane, and decane. In addition, the method is extended to predict partial molar volumes and enthalpies of adsorbed phases [2], even in rigid simulation boxes, so long as the populations of all adsorbing species are fluctuating. Different approaches for estimating statistical uncertainty are assessed, and property predictions are compared to those from equations of state.

[1] D. B. Harwood, C. J. Peters, and J. I. Siepmann, Fluid Phase Equil., 407, 269-279, (2016).

[2] R.F. DeJaco, B. Elyassi, M. D. de Mello, N. Mittal, M. Tsapatsis, and J. I. Siepmann, J. Chem. Phys., 149, 072331 (2018).