(749d) Transferable Intermolecular Potential Models for a Broad Range of Organic Compounds | AIChE

(749d) Transferable Intermolecular Potential Models for a Broad Range of Organic Compounds

Authors 

Elliott, J. R. - Presenter, The University of Akron

Vapor pressure and liquid density are used to characterize step potentials for fluorinated, chlorinated, brominated, and iodinated hydrocarbons, along with a variety other compounds, bringing the transferable database to over 349 training compounds, 112 of which are added in this manuscript, and 25 "validation" compounds.  The potentials were characterized by 4-step potentials consistent with those of previous studies for the SPEADMD model.  Vapor pressure deviations average near 10% for most compounds in the training set and near 40% for the validation set.  Higher deviations appear in the validation set for compounds in which multiple functionalities are located in close proximity, indicating sensitivity to the transferability assumption in these cases.  Deviations in liquid density approach 4%, despite the large shifts in density caused by the relatively heavy halogenated atoms.  The availability of transferable potentials for so many compounds sets the stage for systematic studies of phase behavior over a broad range of polarity.

In the context of this study we find that a key consideration is to establish a framework for continual revision and improvement. The key elements of this framework are:

  1. A critically evaluated pure component database for training the transferable estimates of the intermolecular interactions.
  2. A critically evaluated training set for mixtures.
  3. A critically evaluated database (or two) for validating the potential models.
  4. An automated system of inferring the molecular interactions from the database.
  5. An intermolecular potential model capable of reproducing the key phenomenology.
  6. A methodology to refine the molecular interactions beyond the transferability assumption.

We demonstrate the implementation of these elements in the context of the SPEADMD model and in the process establish a publicly available database for training sets and validation of future potential models.

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