Group contribution (GC) methods to predict thermochemical properties are of eminent importance process design. Compared previous works, we present an improved group parametrization for the heat formation organic molecules exhibiting chemical accuracy, i.e., a maximum 1 kcal/mol (4.2 kJ/mol) difference between experiment and model, while, at same time, minimizing number parameters. The latter is...

Quantitative structure–property relationships (QSPR) for calculating temperature dependence of surface tension (σ) ionic liquids (ILs) in terms group contributions (GCs) is proposed and broadly presented. A statistical learning method including stepwise multiple linear regression two machine methods feed-forward artificial neural network least-squares support vector was applied to express σ as ...