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

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.

Choosing the right molecular machine learning potential

“…(The force acting on the nuclei is the negative of the PES gradient.) Owing to its generalization capabilities and fast prediction on unseen data, MLPs can be explored to accelerate minimum-energy [10][11][12][13][14][15] and transition-state structure search, 13,16,17 vibrational analysis, 18-21 absorption 22,23 and emission spectra simulation, 24 reaction 13, 25,26 and structural transition exploration, 27 and ground- [3][4][5][6][7][8][9] and excitedstate dynamics propagation. 28,29 A blessing and a curse of ML is that it is possible to design, for all practical purposes, an infinite number of MLP models that can describe a molecular PES.…”

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

“…These values were chosen to be consistent with previous works using the Deep Potential model. 38,43 In addition, we consider different initial data used in the ML potential training. Specifically, we consider: 1) "MD@298", traditional MD at 298K, 2) "TREMD@298,(315),(330)", enhanced sampling with TREMD at 298K, and 3) "TREMD@298,315,330", TREMD using data at 298K in addition to data from elevated temperatures 315K and 330K.…”

“…[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Recently, an ML-based model called Deep Potential -Smooth Edition (DeepPot-SE) 36 was developed to efficiently represent organic molecules, metals, semiconductors and insulators with an accuracy comparable to that of ab initio QM models. The DeepPot-SE model has recently been highlighted in simulations of interfacial processes in aqueous aerosol 37 and large-scale combustion reactions in the gas phase, 38 and demonstrated great success in providing predictive insight into complex reaction processes. To improve the accuracy and transferability of the DeepPot-SE models, the Deep Potential GENerator (DP-GEN) scheme 39,40 uses an active-learning algorithm to generate models in a way that minimizes human intervention and reduces the computational cost for data generation and model training.…”

A transferable active-learning strategy for reactive molecular force fields

“…Very recently, AL approaches have started to be adopted for fitting reactive potentials for organic molecules based on single point evaluations at quantum-chemical levels of theory. Notable examples include the modelling of gas-phase pericyclic reactions, 12 the exploration of reactivity during methane combustion, 50 and the decomposition of urea in water. 41 In the present work -with a view to developing potentials to simulate solution phase reactions -we consider bulk water as a test case and develop a strategy which requires just hundreds of total ground truth evaluations and no a priori knowledge of the system, apart from the molecular composition.…”