Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 1. Density

Kamil Paduszyński

Abstract

A new group contribution (GC) quantitative structure-property relationship (QSPR) for estimating density (ρ) of pure ionic liquids (ILs) as a function of temperature (T) and pressure (p) is developed on the basis of the most comprehensive collection of volumetric data reported so far (in total 41 250 data points, deposited for 2267 ILs from diverse chemical families). The model was established based on a carefully revised, evaluated, and reduced data set, whereas the adopted GC methodology follows the approach proposed previously [ Ind. Eng. Chem. Res. 2012, 51, 591−604]. However, a novel approach is proposed to model both temperature and pressure dependence. The idea consist of an independent representation of reference density ρ0 at T0 = 298.15 K and ρ0 = 0.1 MPa and dimensionless correction f(T, P) ≡ ρ(T, p)/ρ0 for other conditions of temperature and pressure. Three common machine learning algorithms are employed to represent the quantitative structure–property relationship between the studied property end points, GCs, T, and p, namely, multiple linear regression, feed-forward artificial neural network, and least-squares support vector machine. On the basis of detailed statistical analysis of the resulting models, including both internal and external stability checks by means of common statistical procedures such as cross-validation, y-scrambling, and “hold-out” testing, the final model is selected and recommended. An impact of type of cation and anion of the accuracy of calculations is highlighted and discussed. Performance of the new model is finally demonstrated by comparing it with similar methods published recently in the literature.
Author Kamil Paduszyński (FC / DPC)
Kamil Paduszyński,,
- Department Of Physical Chemistry
Journal seriesIndustrial & Engineering Chemistry Research, ISSN 0888-5885, (A 35 pkt)
Issue year2019
Vol58
No13
Pages5322-5338
Publication size in sheets0.8
ASJC Classification1600 General Chemistry
DOIDOI:10.1021/acs.iecr.9b00130
URL https://pubs.acs.org/doi/10.1021/acs.iecr.9b00130
Languageen angielski
File
acs.iecr.9b00130.pdf 3.41 MB
Score (nominal)35
ScoreMinisterial score = 35.0, 17-07-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.203; WoS Impact Factor: 2017 = 3.141 (2) - 2017=3.284 (5)
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