Login / Signup

Development of New Transferable Coarse-Grained Models of Hydrocarbons.

Yaxin AnKarteek K BejagamSanket A Deshmukh
Published in: The journal of physical chemistry. B (2018)
We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme. This was followed by the development of the nonane model described with hybrid 2-2-3-2 and 3:1 (3-3-3) mapping schemes. The force-field parameters for these three CG models were optimized to reproduce four experimentally observed properties including density, enthalpy of vaporization, surface tension, and self-diffusion coefficient at 300 K. The CG MD simulations conducted with these new CG models of decane and nonane, at different timesteps, for various system sizes, and at a range of different temperatures, were able to predict their density, enthalpy of vaporization, surface tension, self-diffusion coefficient, expansibility, and isothermal compressibility with good accuracy. Moreover, a comparison of structural features obtained from the CG MD simulations and the CG beads of mapped all-atom trajectories of decane and nonane showed very good agreement. To test the chemical transferability of these models, we have constructed the models for hydrocarbons ranging from pentane to heptadecane, by using different combinations of the CG beads of decane and nonane. The properties of pentane to heptadecane predicted by these new CG models showed excellent agreement with the experimental data.
Keyphrases
  • molecular dynamics
  • density functional theory
  • depressive symptoms
  • wastewater treatment
  • computed tomography
  • magnetic resonance imaging
  • machine learning
  • magnetic resonance
  • electronic health record
  • monte carlo