Towards fully ab initio modeling of soot formation in a nanoreactor

Abstract

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 critical processes in soot formation, acetylene polymerization, and inception of PAH radicals. This shows that NNMD can capture the dynamic process of acetylene polymerization into PAH precursors. The simulation of PAH radicals reveals that physical interaction enhances chemical nucleation, and such enhancement is observed for clusters of π- and σ-radicals, which is distinct from the dimer. We also observed that PAH radicals of ~ 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.

Revealing the thermal decomposition mechanism of RDX crystal by a neural network potential

“…Compared to the AIMD method, the simulations using NNP are faster by four orders of magnitude. This significant improvement in the computational costs is consistent with previous works using the NNPs 36 . This can be attributed to the implementation in the neural network combining the-state-of art GPU computing.…”

“…This is expected as the implementation of the NNP is optimized for large systems. Also, it is worth noting that the ReaxFF model exhibits an O(N) scaling rule rather than an O(NlogN) scaling rule, different from previous works in the low-density gas 36 . The better performance of the NNP enables the exploration of a system with tens of thousands or even millions of atoms at the ab initio accuracy, providing a feasible approach to investigate the complex reaction network of RDX crystal from an atomic perspective.…”