Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation

Abstract

High-temperature, reactive gas flow is inherently nonequilibrium in terms of energy and state population distributions. Modeling such conditions is challenging even for the smallest molecular systems due to the extremely large number of accessible states and transitions between them. Here, neural networks (NNs) trained on explicitly simulated data are constructed and shown to provide quantitatively realistic descriptions which can be used in mesoscale simulation approaches such as Direct Simulation Monte Carlo to model gas flow at the hypersonic regime. As an example, the state-to-state cross sections for N(4S) + NO(2Π) → O(3P) + N2(X1Σg+) are computed from quasiclassical trajectory (QCT) simulations. By training NNs on a sparsely sampled noisy set of state-to-state cross sections, it is demonstrated that independently generated reference data are predicted with high accuracy. State-specific and total reaction rates as a function of temperature from the NN are in quantitative agreement with explicit QCT simulations and confirm earlier simulations, and the final state distributions of the vibrational and rotational energies agree as well. Thus, NNs trained on physical reference data can provide a viable alternative to computationally demanding explicit evaluation of the microscopic information at run time. This will considerably advance the ability to realistically model nonequilibrium ensembles for network-based simulations.

Accurate reproducing kernel-based potential energy surfaces for the triplet ground states of N2O and dynamics for the N + NO ↔ O + N2 and N2 + O → 2N + O reactions

“…Recently, a state-to-state cross section model for the forward reaction based on the 3 A 0 PES has been developed using a neural network which predict the reaction rates and product state distributions accurately. 35 Reaction rates obtained from the PESs using fits to the CASPT2/ cc-pVTZ data 26 are in good agreement with the experiments. 26,31,32 However, the vibrational relaxation rates for the reverse reaction are significantly smaller than the experimental results up to 7000 K. 33 The PESs in ref.…”

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

“…However, MD simulations involving excited states are highly nontrivial, and there are large uncertainties in ab initio quantum chemistry computation for treating excited states of large systems. Based on sophisticated empirical or machine-learning PESs, several recent works have achieved the excited-state MD simulation for model systems [54][55][56][57][58][59][60][61][62] . For example, the O+O recombination reaction to form the ground and excited-state singlet O 2 molecules on amorphous solid water 60 .…”

Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

“…Although MD simulations involving excited states are highly non-trivial, some recent work has made promising progress in relatively small systems. [52][53][54][55][56][57][58] Through further development, these methods are expected to be used in more complex pyrolysis or combustion systems.…”