Finding Thermodynamically Favorable Pathways in Chemical Reaction Networks Using Flows in Hypergraphs and...

Area of Science:

• Chemical Engineering
• Computational Chemistry
• Systems Chemistry

Background:

• Optimizing chemical reaction pathways is crucial for chemical synthesis and reactor design.
• Chemical reaction networks are often modeled as hypergraphs for computational analysis.
• Existing pathway search methods can be enhanced by incorporating thermodynamic principles.

Purpose of the Study:

• To develop a novel computational framework for identifying thermodynamically favorable chemical pathways.
• To integrate chemical potentials and concentrations into pathway search algorithms.
• To enable ranking of alternative pathways based on thermodynamic criteria.

Main Methods:

• A mixed-integer linear programming (mixed ILP) formulation was developed to represent the pathway search problem.
• Thermodynamic principles, including chemical potentials and concentrations, were integrated into the mixed ILP model.
• The framework was applied to the HCN-formamide chemistry network.

Main Results:

• The method successfully constrained pathway searches to include only thermodynamically favorable reactions.
• Alternative pathways to the hypothesized HCN-formamide chemistry pathway were enumerated.
• Identified pathways were ranked using objective functions based on thermodynamics, with some scoring higher than the literature-proposed pathway.

Conclusions:

• The proposed framework effectively enriches pathway search methods with thermodynamic principles.
• This integration allows for the discovery and ranking of optimized chemical pathways based on thermodynamic favorability.
• The approach has significant implications for chemical synthesis, reactor optimization, and understanding complex chemical systems.