Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
J Phys Chem A. 2021 May 20;125(19):4233-4244. doi: 10.1021/acs.jpca.1c00942. Epub 2021 May 11.
The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.
评估原子相互作用的计算成本很高，这促使人们开发出计算成本较低的动力学模型，这些模型可以通过对数千种物质或其他过程的复杂化学的分子动力学（MD）模拟进行参数化，从而将化学演化的预测速度提高多达四个数量级。与常用的势能面拟合方法不同，这些模型纯粹旨在描述动力学效应。到目前为止，这种动力学模型利用了反应的分子描述，并仅限于再现 MD 模拟中观察到的分子。因此，这些描述无法预测未观察到的分子的反应性，例如在大分子或固体的情况下。在这里，我们提出了一种从 MD 模拟中提取反应机制和反应速率的新方法，即使用原子级特征。以碳氢化合物热解的复杂化学网络为例，证明了使用原子特征构建的动力学模型能够探索在用于参数化的 MD 模拟中从未观察到的化学反应途径，这是描述罕见事件的关键特征。原子级特征被用来构建反应机制，并从基本原子事件估算未知分子的反应速率。通过比较模型在比参数化所用的模拟时间尺度和化学组成更长的时间尺度上进行外推的能力，证明了使用原子特征的动力学模型保留了与基于分子物种的特征相同的准确性和可转移性，同时更紧凑，并且用更少的数据进行参数化。我们还发现，原子特征可以更好地描述大分子的形成，从而能够同时描述小分子和凝聚相。
