Comparison of Computational Methods for Simulating Depolymerization Reaction.

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Chemical recycling is vital for a circular economy, converting polymers back to raw materials.
• Simulating polymer depolymerization is key to developing efficient recycling processes.

Purpose of the Study:

• To develop and validate a reactive molecular dynamics (MD) system for simulating polymer-to-monomer depolymerization.
• To compare the performance of neural network potential (NNP) and Reax force field (ReaxFF) in simulating polystyrene depolymerization.

Main Methods:

• Utilized reactive molecular dynamics (MD) simulations.
• Employed two potential models: neural network potential (NNP) and Reax force field (ReaxFF).
• Simulated the depolymerization of a polystyrene model and compared results with experimental data.

Main Results:

• NNP-MD accurately replicated polystyrene degradation and redecomposition, showing consistency with experimental data across various temperatures.
• ReaxFF-MD demonstrated limitations in representing the depolymerization process accurately.
• Monomer yields and decomposition products from NNP-MD simulations aligned well with experimental observations.

Conclusions:

• NNP-MD is a reliable method for simulating polymer depolymerization, yielding results consistent with experimental findings.
• The developed NNP-MD system contributes to advancing chemical recycling technologies.
• This research supports the broader implementation of circular economy principles through efficient polymer recycling.