NH2与烷基自由基的重组:神经网络电位的VRC-TST速率常数

IF 5.2 2区 工程技术 Q2 ENERGY & FUELS
Simone Vari, Carlo Cavallotti
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引用次数: 0

摘要

CH3与NH2的复合反应是描述碳氢化合物与氮化合物在燃烧过程中形成化学键的重要参考反应。例如,当氨和碳氢化合物混合物一起燃烧时。尽管该反应在燃烧过程中起着重要的作用,但关于准确测定其速率常数或压力依赖性的理论研究却很有限。目前,大多数现有的动力学机制都是在室温下进行的实验测量,或者在高温下对逆向过程进行详细的平衡和速率常数测量,因此在反应速率依赖于压力的条件下。这就限制了准确描述两种关键自由基甲基和NH2的反应活性,特别是当这种反应途径与其他反应途径竞争时。本工作旨在填补这一空白,为n = 1,2,3的CnH2n+1 + NH2反应族的重组途径提供ab-initio速率常数估计。速率常数估计使用可变反应坐标-过渡状态理论(VRC-TST)和机器学习。VRC-TST是无势垒反应动力学研究的黄金标准,无势垒反应没有明确的过渡态。用VRC-TST估计的速率常数接近实验精度,但代价是对反应势能面(PES)进行计算要求很高的蒙特卡罗采样。在这项工作中,我们使用人工神经网络(ANN)来学习与感兴趣的反应相关的多维PES部分,作为描述两个反应片段相对方向的自由度的函数。基于物理的人工神经网络架构大大减少了VRC-TST所需的显式电子结构计算的数量,从而在不影响准确性的情况下节省了大量时间。计算得到的速率常数与实验数据吻合较好,可望为氮化合物与烃类共燃烧的动力学建模提供有益的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recombination of NH2 with alkyl radicals: VRC-TST rate constants from neural network potentials
The recombination between CH3 and NH2 is an important reference reaction for describing the formation of chemical bonds between hydrocarbons and nitrogen compounds in combustion. This is for example the case when ammonia is burned together with hydrocarbon mixtures. Despite the important role played by this reaction in combustion processes, the theoretical studies on the accurate determination of its rate constant, or on the pressure dependence, are limited. At present, most existing kinetic mechanism use experimental measures performed at room temperatures, or detailed balance and the rate constants measured for the reverse process at high temperatures, thus in conditions in which the reaction rate is pressure dependent. This places some limits on the ability to accurately describe the reactivity of two key radical species: methyl and NH2, in particular when this reaction pathway is in competition with others. The present work aims at filling this gap, providing ab-initio rate constant estimations for the recombination pathway of the reaction family CnH2n+1 + NH2, with n = 1, 2, 3. Rate constants are estimated using Variable Reaction Coordinate – Transition State Theory (VRC-TST) and machine learning. VRC-TST is the golden standard for kinetic studies of barrierless reactions, which do not have a well-defined transition state. The rate constants estimated with VRC-TST approach the experimental accuracy, at the cost of a computationally demanding Monte Carlo sampling of the reactive Potential Energy Surface (PES). In this work we use Artificial Neural Network (ANN) to learn the portion of the multidimensional PES relevant to the reaction of interest as a function of the degrees of freedom describing the relative orientation of the two reacting fragments. The physics-informed ANN architecture significantly reduces the number of explicit electronic structure calculations needed by VRC-TST, thus gaining significant time savings without compromising accuracy. The calculated rate constants are in good agreement with the available experimental data and are thus expected to provide a useful reference for the kinetic modelling of the co-combustion of nitrogen compounds and hydrocarbons.
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来源期刊
Proceedings of the Combustion Institute
Proceedings of the Combustion Institute 工程技术-工程:化工
CiteScore
7.00
自引率
0.00%
发文量
420
审稿时长
3.0 months
期刊介绍: The Proceedings of the Combustion Institute contains forefront contributions in fundamentals and applications of combustion science. For more than 50 years, the Combustion Institute has served as the peak international society for dissemination of scientific and technical research in the combustion field. In addition to author submissions, the Proceedings of the Combustion Institute includes the Institute''s prestigious invited strategic and topical reviews that represent indispensable resources for emergent research in the field. All papers are subjected to rigorous peer review. Research papers and invited topical reviews; Reaction Kinetics; Soot, PAH, and other large molecules; Diagnostics; Laminar Flames; Turbulent Flames; Heterogeneous Combustion; Spray and Droplet Combustion; Detonations, Explosions & Supersonic Combustion; Fire Research; Stationary Combustion Systems; IC Engine and Gas Turbine Combustion; New Technology Concepts The electronic version of Proceedings of the Combustion Institute contains supplemental material such as reaction mechanisms, illustrating movies, and other data.
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