Computational material synthesis approach for characterising flame-retardant mechanisms of composite polymers

When it comes to the fabrication of the next generation of advanced building polymers, it is imperative to establish a systematic approach to aid the material design process that considers atomistic interactions. Whilst most recently developed materials are nanocomposites such as nano-particles, nano-sheets or nano-tubes. To enhance our understanding of the properties of such materials, it is essential to develop computational models to simulate the thermal degradation, gas deposition and flame resistance of the polymer composites, where data is achieved by molecular dynamics (MD) simulations. Currently, most of the research on flame-retardant chemicals remains qualitative, built upon decades of experimental knowledge. Model development to strengthen our understanding of flame retardancy remains in its early stages, especially for emerging nanocomposite materials. To bridge the knowledge gap, Molecular Dynamics (MD) simulations coupled with the Reactive Force Field (ReaxFF) can be used to gain a more in-depth knowledge of the pyrolysis breakdown of a material. MD simulation is a computational method based on statistical mechanics and thermodynamics theory to simulate the interactions and behaviours of various atoms and molecules. Atomistic-scale computational techniques provide a powerful tool for investigating the material properties and the thermal degradation process of polymer composites, especially the emission of volatile and toxic gas species because chemical reactions can be observed at the molecular level. MD simulations of polymeric materials such as high-density polyethene (PE). To generate quality pyrolysis kinetics to enhance the accuracy of the fire model, a systematic framework to extract TGA data is proposed involving the Kissinger–Akahira–Sunose method followed by the Genetic Algorithm, with less than 5% of RMS error against experimental data. Furthermore, a framework was proposed to validate the foam samples' kinetics through fundamental modelling using the Molecular Dynamics (MD) technique (Fig.1). With the application of MD modelling of the fundamental breakdown of the foam samples system, the detailed concentrations of gas volatiles involved in the solid decomposition process were comprehensively described and can be used as inputs to the numerical fire predictive model. Prediction demonstrated that this approach is viable with reasonable agreements with experimental heat release rate, gas concentration and smoke data.