The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. Issue 54 (18th October 2021)

• Record Type: Journal Article
• Authors: Lotfi, Shahram; Ahmadi, Shahin; Kumar, Parvin
• Is Part Of: RSC advances. Volume 11:Issue 54(2021)
• Page Start: 33849
• Page End: 33857
• Publication Date: 2021-10-18
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1ra06861j
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. Abstract : Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point ( T m ). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values T m of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as R Validation 2, Q Validation 2, and IICValidation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of T m are also extracted.