Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Jinzhe Zeng,Liqun Cao,Tong Zhu,Mingyuan Xu,John Z H Zhang

doi:10.1038/s41467-020-19497-z

Abstract

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

Highlights

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process
We present an in silico simulation of methane combustion based on an neural networks (NN) potential derived by training a highdimensional NN model from ab initio computed energies
The DeepPot-SE model[47] was used to train the NN potential energy surface (PES) based on the reference

Summary

Introduction

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish. To achieve high efficiency and accuracy, the DeePMD model was used[39,40,41] This NN PES can accurately predict the energy and atomic forces of reactants, products and reaction intermediates. Based on this model, a 1-ns reactive MD simulation was performed for a combustion system initially containing 100 methane and 200 oxygen molecules with a sub-femtosecond time resolution (Fig. 1). The simulation produced the main reaction pathways that are consistent with the experiment and provided much more detailed insights about the combustion processes as will be described in the following

Methods

Results

Conclusion