Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data

Area of Science:

• Chemical Engineering
• Computational Chemistry
• Reaction Kinetics

Background:

• Deriving reaction mechanisms and kinetic models from atomistic simulations is crucial but challenging for chemical reaction engineering.
• Accurate kinetic models are essential for designing and optimizing chemical processes.

Purpose of the Study:

• To develop a novel methodology for determining reaction mechanisms and kinetic models from atomistic simulation data.
• To simplify complex reaction networks into skeletal networks suitable for kinetic modeling.
• To validate the derived kinetic models against simulation and experimental data.

Main Methods:

• A three-component approach: (1) identifying species/reactions and calculating rate constants, (2) reducing complex networks to skeletal ones, and (3) validating full and skeletal models.
• Application of the methodology to hydrogen combustion simulation data.
• Statistical calculation of reaction rate constants and deterministic model validation.

Main Results:

• Successfully derived a full reaction network (69 species, 256 reactions) and a reduced skeletal network (9 species, 30 reactions) for hydrogen combustion.
• Both full and skeletal kinetic models accurately represented the simulation data.
• Essential elementary reactions and rate constants showed favorable agreement with experimental findings.

Conclusions:

• The developed methodology effectively determines chemical reaction mechanisms and kinetic models from atomistic simulations.
• Network reduction simplifies complex systems for practical kinetic modeling without sacrificing accuracy.
• The approach provides a reliable tool for chemical reaction engineering and design, validated by experimental data.