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Data-Driven Many-Body Models with Chemical Accuracy for CH4/H2O Mixtures.

Marc RieraAlan HiralesRaja GhoshFrancesco Paesani
Published in: The journal of physical chemistry. B (2020)
Many-body potential energy functions (PEFs) based on the TTM-nrg and MB-nrg theoretical/computational frameworks are developed from coupled cluster reference data for neat methane and mixed methane/water systems. It is shown that the MB-nrg PEFs achieve subchemical accuracy in the representation of individual many-body effects in small clusters and enables predictive simulations from the gas to the liquid phase. Analysis of structural properties calculated from molecular dynamics simulations of liquid methane and methane/water mixtures using both TTM-nrg and MB-nrg PEFs indicates that, while accounting for polarization effects, is important for a correct description of many-body interactions in the liquid phase, an accurate representation of short-range interactions, as provided by the MB-nrg PEFs, is necessary for a quantitative description of the local solvation structure in liquid mixtures.
Keyphrases
  • ionic liquid
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  • carbon dioxide
  • high resolution
  • molecular dynamics
  • big data
  • artificial intelligence
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