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First-principles–based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition

机译:反应性分子动力学模拟中基于第一原理的反应动力学:在过氧化氢分解中的应用

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摘要

This paper presents our vision of how to use in silico approaches to extract the reaction mechanisms and kinetic parameters for complex condensed-phase chemical processes that underlie important technologies ranging from combustion to chemical vapor deposition. The goal is to provide an analytic description of the detailed evolution of a complex chemical system from reactants through various intermediates to products, so that one could optimize the efficiency of the reactive processes to produce the desired products and avoid unwanted side products. We could start with quantum mechanics (QM) to ensure an accurate description; however, to obtain useful kinetics we need to average over ∼10-nm spatial scales for ∼1 ns, which is prohibitively impractical with QM. Instead, we use the reactive force field (ReaxFF) trained to fit QM to carry out the reactive molecular dynamics (RMD). We focus here on showing that it is practical to extract from such RMD the reaction mechanisms and kinetics information needed to describe the reactions analytically. This analytic description can then be used to incorporate the correct reaction chemistry from the QM/ReaxFF atomistic description into larger-scale simulations of ∼10 nm to micrometers to millimeters to meters using analytic approaches of computational fluid dynamics and/or continuum chemical dynamics. In the paper we lay out the strategy to extract the mechanisms and rate parameters automatically without the necessity of knowing any details of the chemistry. We consider this to be a proof of concept. We refer to the process as RMD2Kin (reactive molecular dynamics to kinetics) for the general approach and as ReaxMD2Kin (ReaxFF molecular dynamics to kinetics) for QM-ReaxFF–based reaction kinetics.
机译:本文介绍了我们如何使用计算机方法提取复杂冷凝相化学过程的反应机理和动力学参数的构想,这些过程是从燃烧到化学气相沉积等重要技术的基础。目的是提供对复杂化学系统从反应物通过各种中间体到产物的详细演变的分析描述,以便可以优化反应过程生产所需产物的效率,并避免不希望的副产物。我们可以从量子力学(QM)开始,以确保描述准确。但是,为了获得有用的动力学,我们需要在约10 nm的空间尺度上平均约1 ns,这对于QM来说是不切实际的。相反,我们使用经过训练以适合QM的反作用力场(ReaxFF)来执行反作用分子动力学(RMD)。我们在这里集中于表明从这种RMD提取分析描述反应所需的反应机理和动力学信息是可行的。然后,可以使用计算流体动力学和/或连续化学动力学的分析方法,使用这种分析描述将来自QM / ReaxFF原子描述的正确反应化学方法合并到10纳米至微米至毫米至米的大规模模拟中。在本文中,我们提出了一种策略,可自动提取机理和速率参数,而无需了解任何化学细节。我们认为这是概念的证明。对于通用方法,我们将此过程称为RMD2Kin(动力学的动力学分子动力学),对于基于QM-ReaxFF的反应动力学,我们将其称为ReaxMD2Kin(动力学的ReaxFF分子动力学)。

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