Toward full ab initio modeling of soot formation in a nanoreactor

A neural network (NN)-based model is proposed to construct the potential energy surface of soot formation. Our NN-based model is proven to possess good scalability of O(N) and retain the ab initio accuracy, which allows the investigation of the entire evolution of soot particles with tens of nm from an atomic perspective. A series of NN-based molecular dynamics (NNMD) simulations are performed using a nanoreactor scheme to investigate the critical process in soot formation - the inception of PAH radicals. The results show that physical interaction enhances chemical inception, and such enhancement is observed for clusters of pi- and sigma-radicals, which are distinct from the dimer. We also observed that PAH radicals of similar to 400 Da can produce core-shell soot particles at a flame temperature, with a disordered core and outer shell of stacked PAHs, suggesting a potential physically stabilized soot inception mechanism.

Automated summary

We propose a neural network-based model to construct the potential energy surface of soot formation and investigate the critical process of PAH radical inception through molecular dynamics simulations. The results show that physical interaction enhances chemical inception and this enhancement is observed in clusters of pi- and sigma-radicals.