Literature DB >> 18412410

Quantum mechanically based estimation of perturbed-chain polar statistical associating fluid theory parameters for analyzing their physical significance and predicting properties.

Nguyen Van Nhu1, Mahendra Singh, Kai Leonhard.   

Abstract

We have computed molecular descriptors for sizes, shapes, charge distributions, and dispersion interactions for 67 compounds using quantum chemical ab initio and density functional theory methods. For the same compounds, we have fitted the three perturbed-chain polar statistical associating fluid theory (PCP-SAFT) equation of state (EOS) parameters to experimental data and have performed a statistical analysis for relations between the descriptors and the EOS parameters. On this basis, an analysis of the physical significance of the parameters, the limits of the present descriptors, and the PCP-SAFT EOS has been performed. The result is a method that can be used to estimate the vapor pressure curve including the normal boiling point, the liquid volume, the enthalpy of vaporization, the critical data, mixture properties, and so on. When only two of the three parameters are predicted and one is adjusted to experimental normal boiling point data, excellent predictions of all investigated pure compound and mixture properties are obtained. We are convinced that the methodology presented in this work will lead to new EOS applications as well as improved EOS models whose predictive performance is likely to surpass that of most present quantum chemically based, quantitative structure-property relationship, and group contribution methods for a broad range of chemical substances.

Entities:  

Year:  2008        PMID: 18412410     DOI: 10.1021/jp7105742

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  1 in total

1.  Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers.

Authors:  Mingzhe Chi; Rihab Gargouri; Tim Schrader; Kamel Damak; Ramzi Maâlej; Marek Sierka
Journal:  Polymers (Basel)       Date:  2021-12-22       Impact factor: 4.329

  1 in total

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