Discrete Modeling Approach for Cluster-Based Excess Gibbs-Energy of Molecular Liquids.

Area of Science:

• Thermodynamics
• Statistical Mechanics
• Computational Chemistry

Background:

• Modeling liquid mixtures requires understanding complex molecular interactions.
• Existing models often struggle to capture the geometric nuances of local molecular arrangements.
• Accurate prediction of excess Gibbs energy is crucial for chemical process design.

Purpose of the Study:

• To develop a novel model for excess Gibbs energy in binary liquid mixtures.
• To incorporate three-dimensional molecular geometry into thermodynamic modeling.
• To provide a framework for predicting mixture behavior based on molecular clusters.

Main Methods:

• Modeling excess Gibbs energy using discrete molecular clusters.
• Employing a discrete Markov-chain approach for mixture construction.
• Utilizing constrained minimization of Helmholtz free energy to determine cluster probabilities.
• Applying informational Shannon entropy synonymously with thermodynamic entropy.
• Introducing a molecular sampling algorithm with a force-field approach for interaction energies.

Main Results:

• The model successfully describes various excess Gibbs energy curves for four exemplary mixtures.
• The approach demonstrates the ability to differentiate between structural isomers.
• Promising results indicate the model's potential for accurate thermodynamic predictions.

Conclusions:

• The discrete cluster model provides a robust method for predicting excess Gibbs energy in liquid mixtures.
• Incorporating geometric information enhances the accuracy of thermodynamic predictions.
• The developed molecular sampling algorithm facilitates the application of the model to real systems.