Open Access Dissertation
Date of Award
Chemical Engineering and Materials Science
Jeffrey J. Potoff
In this work, to address the sampling problem for systems at high densities and low temperatures, a generalized identity exchange algorithm is developed for grand canonical Monte Carlo simulations. The algorithm, referred to as Molecular Exchange Monte Carlo (MEMC), is implemented in the GPU-Optimized Monte Carlo (GOMC) software and may be applied to multicomponent systems of arbitrary molecular topology, and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion/deletion of the large molecules, and the pros and cons of each method are discussed. Next, the MEMC method is extended to Gibbs ensemble Monte Carlo (GEMC). The utility of the MEMC method is demonstrated through the calculation of the free energies of transfer of n-alkanes from vapor into liquid 1-octanol, n-hexadecane, and 2,2,4-trimethylpentane, using isobaric-isothermal GEMC simulations.
Alternatively, for system with strong inter-molecular interaction (e.g. hydrogen bonds), it’s more efficient to calculate the free energies of transfer, using standard thermodynamic integration (TI) and free energy perturbation (FEP) methods. The TI and FEP free energy calculation methods are implemented in GOMC and utility of these methods are demonstrated by calculating the hydration and solvation free energies of fluorinated 1-octanol, to understand the role of fluorination on the interactions and partitioning of alcohols in aqueous and organic environments.
Additionally, using GOMC, a transferable united-atom (UA) force field, based on Mie potentials, is optimized for alkynes to accurately reproduce experimental phase equilibrium properties. The performance of the optimized Mie potential parameters is assessed for 1-alkynes and 2-alkynes using grand canonical histogram-reweighting Monte Carlo simulations. For each compound, vapor-liquid coexistence curves, vapor pressures, heats of vaporization, critical properties, and normal boiling points are predicted and compared to experiment.
Soroush Barhaghi, Mohammad, "Force Field Optimization, Advanced Sampling, And Free Energy Methods With Gpu-Optimized Monte Carlo (gomc) Software" (2019). Wayne State University Dissertations. 2337.