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A semiempirical model based on simple physical assumptions is rigorously compared to a combination of two recent state-of-the-art quantitative structure–property relationship (QSPR) methods for impact sensitivities of nitroaliphatic and nitramine compounds. For most datasets considered, it yields slightly better predictions than QSPR schemes, which is noteworthy, considering the fact that it relies only on three adjustable parameters and an equation developed independently of the data. Further advantages of this approach include physical interpretability, enhanced robustness, and wider applicability. Therefore, there is no doubt that such physics-based models provide valuable alternatives to the purely empirical relationships usually employed in regulatory contexts, especially in situations where experimental data are scarce.

The interaction of solvents with ultrasonic waves is of drastic importance and has been the subject of many studies in recent years. In this study, the effect of solvent structural parameters on the speed of sound in chemical solvents was investigated through a quantitative structure–property relationship (QSPR). Genetic algorithm–multiple linear regression (GA-MLR) analysis was employed to select the most relevant subset of descriptors and, then, to develop the model. The validity of the obtained 10-parameter model was assessed by most widely used validation techniques. The predictive power of the model was evaluated by use of an external data set. The high level of accuracy of results approved the model. According to the model, those solvents that have stronger solvent–solvent interactions can create a more appropriate medium for passing and propagating sound waves and will result in higher speed of sounds.

In the present work, a quantitative structure–property relationship study is performed to predict gas heat capacity for a structurally wide variety of organic compounds using the genetic function approximation (GFA) and the adaptive neuro-fuzzy inference system (ANFIS) methods. The simple proposed models contain only three descriptors calculated solely from the molecular structure of compounds which are 3D-independent descriptors. The models were validated by an external prediction set. Good results were obtained from both models which get the squared correlation coefficients of 0.996 and 0.997 for GFA and ANFIS, respectively. This study discloses enhanced correlations of the heat capacity of gases with their molecular structures, wherein the influence of the size of molecules is found to predominate.

Novel QSPR models were developed and evaluated for the prediction of heat capacity of liquids at 298.15 K with only three descriptors. Two linear and nonlinear models were produced using genetic function approximation (GFA) and adaptive neurofuzzy inference system (ANFIS) methods based on a data set of 706 compounds with a wide variety of functional groups. The results showed that both GFA and ANFIS methods could model the relationship between the liquid heat capacity of organic compounds and their structures with high accuracy. The predictive quality of the QSPR models were tested for an external test set, where the squared correlation coefficients of prediction for the GFA and ANFIS methods were 0.970 and 0.973, respectively.

Quantitative structure–property relationship (QSPR) modeling of normal boiling point temperature (Taz) and the composition (weight fraction, X1w) of 176 binary azeotropic mixtures was performed using ensemble multiple linear regression analysis and fragment descriptors implemented in ISIDA software. The models have been validated in external 5-fold cross-validations procedure and on an additional test set of 24 azeotropes. The prediction errors (3–4 K for Taz and 10–14 wt % for X1w) are comparable with the noise in experimental data. A simple empirical relationship linking Taz with boiling points of two molecular components of azeotrope has been suggested.

In the chemical process industry, hazardous chemical dusts have recently been the cause of many severe incidents leading to fatalities and injuries. It is important to characterize the hazards posed by dust materials. Thus, in this work the quantitative structure property relationship (QSPR) technique has been used to predict the explosibility characteristics (i.e., KSt, Pmax) of chemical dusts. The data set consisted of 31 chemical dusts, further divided into training and test sets. The mathematical models were developed using Material Studio 5.0 software and the genetic function approximation (GFA) algorithm. The final predictive models for the maximum overpressure (Pmax) and dust deflagration index (KSt) are cubic equations with 5 parameters and high correlation coefficients (R2 for the models are 0.96 and 0.91, respectively). The correlations also took into account the effect of the dust particle size by using the median value as a parameter in the developed models. This is essential because dust expl...

One of the industrially important thermodynamic properties of polymer solutions is the upper critical solution temperature at the limit of infinite chain length of polymers, which is used to realiz...