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- 2011 Annual Meeting
- Engineering Sciences and Fundamentals
- Development of Intermolecular Potential Models
- (289a) Efficient Determination of Force Field Parameters Using a Physically Based Equation of State
In this work, we present a novel method to significantly reduce the computational burden required for fitting force field parameters to experimental VLE data. In sharp contrast to conventional optimization approaches, we use additional physical insight on how computed VLE data depends on the force field parameters in molecular simulations. We use an analytical equation of state, the perturbed chain statistical associating fluid theory (PC-SAFT) [4], to predict the results of Gibbs Ensemble molecular simulations without actually performing these simulations. We show that all parameters in the PC-SAFT approach are directly related to the force field parameters, thereby making it possible to optimize force field parameters in a different, much faster framework (i.e. PC-SAFT). Since PC-SAFT calculates VLE data at least a factor 106 faster than molecular simulation, the presented optimization procedure results in a drastic decrease of the computational burden.
As a proof of concept, the method is illustrated by optimizing transferable Lennard-Jones parameters for the n-alkane series. The target is to obtain united atom force field parameters that describe the vapor-liquid part of the phase diagrams of n-butane and n-hexane. Optimizing a vector p of 4 force field parameters p = (εCH2CH2, εCH3CH3, σCH2CH2, σCH3CH3) we obtain excellent agreement of coexisting densities, vapor pressure and caloric properties using only two Gibbs ensemble simulations. It is important to note that as we use additional physical insight in the optimization procedure, the number of molecular simulations needed hardly depends on the initial guess for the vector p, i.e. choosing initial force field parameters that deviate 40% from the optimal values leads to an almost equally efficient convergence.
References
[1] Frenkel, D.; Smit, B., Understanding Molecular Simulation: from Algorithms to Applications, 2nd ed. Academic Press; San Diego, 2002.
[2] Allinger, N.L.; Yuh, Y.H.; Lii, J.H. J. Am. Chem. Soc. 1989, 111, 8551-8566.
[3] Martin, M.G.; Siepmann, J.I. J. Phys. Chem. B 1998, 102, 2569-2577.
[4] Gross, J.; Sadowski, G. Ind. Eng. Chem. Res. 2001, 40, 1244-1260.