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

Jina Lee¹, Wangyun Won², Jun-Woo Kim¹

¹CJ BIO Research Institute, CJ CheilJedang Corp., Suwon-Si, Gyeonggi-do 16495, Republic of Korea.

|March 18, 2024

Summary

This summary is machine-generated.

A new Python algorithm estimates molecular properties like boiling point and viscosity using SMILES strings and dipole moments. It offers accurate predictions for substances not in existing databases, serving as a valuable reference.

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Assembly and Characterization of Polyelectrolyte Complex Micelles

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Area of Science:

Chemical Engineering
Computational Chemistry
Physical Chemistry

Background:

Accurate estimation of pure component physical properties is crucial for chemical process design and simulation.
Existing property estimation methods may lack applicability to novel or complex molecules.
The need for reliable predictive tools for a wide range of molecular properties is significant.

Purpose of the Study:

To develop and validate a Python-based algorithm for estimating key physical properties of molecules.
To utilize simplified molecular-input line-entry specification (SMILES) strings and dipole moments as input.
To provide accurate property estimations for molecules absent in current property databases.

Main Methods:

Developed an in-house Python algorithm integrating multiple established models (Joback, Riedel, Gunn-Yamada, Clausius-Clapeyron, Brock-Bird, Letsou-Stiel, Chapman-Enskog-Brokaw, Sato-Riedel, Stiel-Thodos).
Calculated dipole moments using molecular dynamics simulations with the MMFF94 force field in Avogadro software.
Performed a case study on six novel compounds (DHMF, FDA, DEMB, GSH, VITB5, HCYS, AH) not in existing databases.

Main Results:

The Python algorithm accurately estimated normal boiling point, critical properties, standard enthalpy, vapor pressure, liquid molar volume, heat capacity, viscosity, thermal conductivity, and surface tension.
Cross-validation showed near-identical results to Aspen PCES for most parameters, with accurate enthalpy of vaporization predictions via Clausius-Clapeyron.
The algorithm successfully provided property parameters for compounds lacking prior experimental data.

Conclusions:

The developed Python-based algorithm is a reliable tool for estimating diverse pure component property parameters.
It offers a valuable and clear reference, particularly for novel molecules.
The method demonstrates high accuracy and broad applicability in chemical property prediction.