A Postprocessing Tool for Efficient Molecular Reaction Path Analysis in Kinetic Simulations

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Materials Science

Background:

• Analyzing molecular reaction pathways is crucial for understanding chemical processes.
• Traditional methods like DFS and BFS can be computationally intensive and may miss complex reaction dynamics.

Purpose of the Study:

• To develop an efficient, open-source Python tool for extracting molecular reaction pathways from kinetic simulation trajectories.
• To enhance the analysis of molecular reactions by enabling identification across nonadjacent frames.

Main Methods:

• Developed a Python-based tool implementing a novel chain analysis algorithm.
• Utilized a full-time domain response analysis approach.
• Integrated parallel computing for improved processing efficiency and stored results in a directed graph.

Main Results:

• The tool successfully identifies molecular reaction pathways from various simulation types (MD, AIMD, CG).
• Demonstrated application in propyne-ethylene blending and epoxy resin cross-linking systems.
• Achieved significant improvements in processing efficiency through parallel computing.

Conclusions:

• The developed tool offers a portable and widely applicable solution for molecular reaction pathway analysis.
• The chain analysis algorithm and parallel computing enhance the efficiency and comprehensiveness of kinetic simulations.
• The tool has broad potential for diverse molecular systems and reaction studies.