Next generation pure component property estimation models: With and without machine learning techniques

Abstract 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.