Open AccessPredicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures
Author(s) -
Hongpeng He,
Yong Pan,
Jianwen Meng,
Yongheng Li,
Jun-Hong Zhong,
Weijia Duan,
Juncheng Jiang
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c00846
Subject(s) - binary number , ionic liquid , robustness (evolution) , biological system , thermodynamics , linear regression , thermal decomposition , mathematics , set (abstract data type) , quantitative structure–activity relationship , norm (philosophy) , materials science , computer science , algorithm , chemistry , statistics , organic chemistry , physics , biochemistry , arithmetic , machine learning , biology , gene , programming language , catalysis , political science , law
Ionic liquids (ILs) have been regarded as "designer solvents" because of their satisfactory physicochemical properties. The 5% onset decomposition temperature ( T d , 5%onset ) is one of the most conservative but reliable indicators for characterizing the possible fire hazard of engineered ILs. This study is devoted to develop a quantitative structure-property relationship model for predicting the T d , 5%onset of binary imidazolium IL mixtures. Both in silico design and data analysis descriptors and norm index were employed to encode the structural characteristics of binary IL mixtures. The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. The resulting optimal prediction model was a four-variable multiple linear equation, with the average absolute error (AAE) for the external test set being 12.673 K. The results of rigorous model validations also demonstrated satisfactory model robustness and predictivity. The present study would provide a new reliable approach for predicting the thermal stability of binary IL mixtures.