First-principles-based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition
First-principles-based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition
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DOI：
10.1073/pnas.1701383115
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发表时间：
2019-09-10
影响因子：
11.1
通讯作者：
Cheng, Tao
中科院分区：
文献类型：
--
作者：
Ilyin, Daniil V.;Goddard, William A., III;Cheng, Tao
本文介绍了我们的愿景，如何使用在硅片的方法来提取复杂的凝聚相化学过程的反应机理和动力学参数，从燃烧到化学气相沉积的重要技术的基础。其目的是提供一个复杂的化学系统的详细演变的分析描述，从反应物通过各种中间体的产品，使人们可以优化反应过程的效率，以产生所需的产品，并避免不必要的副产物。我们可以从量子力学（QM）开始，以确保准确的描述;然而，为了获得有用的动力学，我们需要在近似10 nm的空间尺度上平均近似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的反应力场（ReaxFF）来进行反应分子动力学（RMD）。我们在这里集中展示，它是实用的提取从这样的RMD的反应机理和动力学信息需要描述的反应分析。然后，该分析描述可以用于将来自QM/ReaxFF原子描述的正确反应化学结合到使用计算流体动力学和/或连续体化学动力学的分析方法的类似于10 nm到微米到毫米到米的更大规模的模拟中。在本文中，我们提出了自动提取机制和速率参数的策略，而无需了解任何化学细节。我们认为这是一个概念的证明。对于一般方法，我们将该过程称为RMD 2Kin（反应分子动力学到动力学），对于基于QM-ReaxFF的反应动力学，我们将其称为ReaxMD 2Kin（ReaxFF分子动力学到动力学）。
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 similar to 10-nm spatial scales for similar to 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 similar to 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.