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Quantitative structure–property relationships (QSPR) between the structure of dyes and the discoloration rate of dyes were established to predict the discoloration rate of dyes in catalytic wet air oxidation (CWAO) process. The DFT-based quantum chemical descriptors were obtained at the B3LYP/6-31G (d,p) level and partial lease squares (PLS) regression was employed for QSPR model development. The discoloration rate of dyes was recorded in the CWAO process by Mo–Zn–Al–O catalyst under room conditions. The optimal QSPR model with a cross-validation Q2(cum) value of 0.845 and R value of 0.9893 indicates that the QSPR model has sufficient predictive ability and robustness. Two components were selected in the model, which account for 0.733 of the variance of the predictor variables and 0.979 of the variance of the dependent variable. The absolute values of W*[1] for that absolute hardness (η) and the most negative atomic net charges of the molecule (q‑) were 0.806657 and 0.561769 demonstrates that η and q− are the main causes for the first component. The second PLS component is loaded on descriptor dipole moment (μ) for which the W*[2] values were 0.906712. The equation was obtained with the descriptors: Y = 51.5042 + 252.644η + 0.899607μ + 102.427q−(Y is the discoloration rate of dyes). The obtained QSPR model could be used for predicting the discoloration rate of dyes and also reveals our previous suspicion of catalytic mechanism.

Ionic liquids (ILs), or ambient-temperature molten salts, comprise ions solely and exhibit several specific liquid-like properties. Research in this area has developed promptly and extensively. Sufficient thermal stability is required when ILs are applied as electrolytes, fire retardants, catalysts, or solvents. Because the thermal decomposition temperature (Td) indicates the maximum processing temperature before undergoing chemical decomposition, it is among the most crucial parameters characterizing their thermal hazard. Apart from actual experimentation, the quantitative structure-property relationship (QSPR) methods have already been applied in forecasting the Td of ILs. In this paper, the possible means of structurally characterizing ILs are comprehensively reviewed. Advances in the QSPR method for predicting IL properties are highlighted, and existing models predicting the Td of ILs are presented. The need for developing new interaction descriptors of ILs for future applications is also emphasized.

We have extended a quantitative structure–property relationship (QSPR) scheme to estimate the auto-ignition temperatures (AIT) of organic compounds by employing GA-ANFIS, PSO-ANFIS, DE-ANFIS and GP methods. The average absolute relative deviations (%AARD) are 7.96, 6.29, 8.85 and 8.26, respectively. The range of these values appears to match that of experimental error in AIT measurements, suggesting strong models. For organic compounds, the AIT can be estimated using the above-mentioned methods, from molecular structure. This goal is possible using only 9 theoretical descriptors.

In this study, the thermal conductivity at different temperatures of ionic liquids has been estimated using a quantitative structure–property relationship (QSPR) model that involved 11 molecular descriptors based on semi-empirical calculations. Thermal conductivity data of ionic liquids (195 experimental data points) were used to obtain the QSPR model for a wide range of temperatures: 273–390 K. The correlative and extrapolative capability of the proposed model was examined using a testing data set not used during the correlation step. The results show that the QSPR model to estimate the thermal conductivity is accurate enough and gives lower deviations than any other QSPR models yet available in the literature with AARD = 1.97 and R 2 = 0.9919.

The present work represents a quantitative structure–property relationship (QSPR) study for predicting the gas-to-ionic liquid partition coefficients (log K) of organic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate based on replacement method (RM) and support vector machine (SVM). The variable selection technique of replacement method (RM) was employed to select the most favorable subset of descriptors from the more than 1000 theoretical descriptors calculated using the Dragon package. The six descriptors selected were used as inputs of SVM to predict the gas-to-ionic liquid partition coefficient of a set of 92 aliphatic and aromatic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate. Results of our computations demonstrate that SVM can be used as a substitute powerful modeling tool for QSPR studies. The log K values calculated by SVM were in good agreement with the experimental data, and the performances of the SVM models were superior to RM one.