Python-Based Algorithm for Estimating the Parameters of Physical Property Models for Substances Not Available in Database.

ABSTRACT: An in-house Python-based algorithm was developed using simplified molecular-input line-entry specification (SMILES) strings and a dipole moment for estimating the normal boiling point, critical properties, standard enthalpy, vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, viscosity, thermal conductivity, and surface tension of molecules. Normal boiling point, critical properties, and standard enthalpy were estimated by using the Joback group contribution method. Vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, and surface tension were estimated by using the Riedel model, Gunn-Yamada model, Clausius-Clapeyron equation, Joback group contribution method, and Brock-Bird model, respectively. Viscosities of liquid and gas were estimated

SUBMITTER: Lee J 

PROVIDER: S-EPMC10938410 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature