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Physiochemical properties of pure components serve as the basis for the design and simulation of chemical products and processes. Models based on the molecular structural information of chemicals for the following 25 pure component properties are presented in this work: (critical‐) temperature, pressure, volume, acentric factor; (normal‐) boiling point, melting point, auto‐ignition temperature; flash point; (standard‐) enthalpy of formation, Gibbs energy of formation, enthalpy of fusion, enthalpy of vaporization, liquid molar volume; (environmental‐) (lethal dose‐) LC50 and LD50, photo‐chemical oxidation potential, bioconcentration factor, permissible exposure limit; (physicochemical‐) acid dissociation constant, water‐solubility, octanol–water partition coefficient, Hildebrandt solubility parameter, Hansen solubility parameters. Utilizing functional groups for molecular representation, two parallel property estimation models where the group contributions for each property are regressed through traditional regression techniques and machine learning techniques are presented. Both techniques use an a priori data analysis before regression of model parameters. A dataset with more than 24,000 chemicals for the 25 pure component properties has been utilized for the development of the two sets of property models. The efficacy of the developed models and their use are highlighted together with a discussion on the overall performance, application range, and predictive capabilities with implications to product and/or process engineering problem solutions.

Thermodynamic properites of molecules are used widely in the study of reactive processes. Such properties are typically measured via experiments or calculated by a variety of computational chemistry methods. In this work, machine learning (ML) models for estimation of standard enthalpy of formation at 298.15 K are developed for three classes of acyclic and closedshell hydrocarbons, viz. alkanes, alkenes, and alkynes. Initially, an extensive literature survey is performed to collect standard enthalpy data for training ML models. A commercial software (Dragon) is used to obtain a wide set of molecular descriptors by providing SMILES strings. The molecular descriptors are used as input features for the ML models. Support vector regression (SVR) and artificial neural networks are used with a two-level K-fold cross-validation (K-fold CV) workflow. The first level is for estimation of accuracy of both the ML models, and the second level is for generation of the final models. The SVR model is selected as the best model based on error estimates over 10-fold CV. The final SVR model is compared against conventional Benson's group additivity for a set of octene isomers from the database, illustrating the advantages of the proposed ML modeling approach.