Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance
Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance
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DOI：
10.1021/acs.jpca.0c04182
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发表时间：
2020-07-16
影响因子：
2.9
通讯作者：
Li, Jun
中科院分区：
文献类型：
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作者：
Lu, Dandan;Behler, Joerg;Li, Jun
H + CH_3OH反应是一个具有多通道和紧密过渡态的典型反应体系，在燃烧和星际介质中起着重要作用。然而，迄今为止，还没有全球可靠的势能面（PES）。在这里，我们开发了全球分析PES为这个系统使用置换不变的多项式神经网络（PIP-NN）和高维神经网络（HD-NN）的方法的基础上，大量的数据点计算的水平上的显式相关的非限制性耦合集群单，双，微扰三重水平与增广的相关校正价三重zeta基组（UCCSD（T）-F12 a/AVTZ）。我们证明了这两种机器学习PES都能够准确地描述所有动态相关的反应通道。在20千卡/摩尔的碰撞能量，准经典轨迹计算表明，占主导地位的通道是从甲基网站的氢提取，产生H-2 + CH 2 OH。该主要通道的反应主要通过直接回弹机制发生。H-2产物的振动和旋转状态都相对较冷，并且大部分可用能量转化为产物的平移运动。
The H + CH3OH reaction, which plays an important role in combustion and the interstellar medium, presents a prototypical system with multi channels and tight transition states. However, no globally reliable potential energy surface (PES) has been available to date. Here we develop global analytical PESs for this system using the permutation-invariant polynomial neural network (PIP-NN) and the high-dimensional neural network (HD-NN) methods based on a large number of data points calculated at the level of the explicitly correlated unrestricted coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-zeta basis set (UCCSD(T)-F12a/AVTZ). We demonstrate that both machine learning PESs are able to accurately describe all dynamically relevant reaction channels. At a collision energy of 20 kcal/mol, quasi-classical trajectory calculations reveal that the dominant channel is the hydrogen abstraction from the methyl site, yielding H-2 + CH2OH. The reaction of this major channel takes place mainly via the direct rebound mechanism. Both the vibrational and rotational states of the H-2 product are relatively cold, and large portions of the available energy are converted into the product translational motion.