School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Department of Physics, City University of Hong Kong, Hong Kong, SAR, People's Republic of China.
Phys Chem Chem Phys. 2023 Feb 15;25(7):5479-5488. doi: 10.1039/d2cp05479e.
As a prototypical system for studying the Eley-Rideal (ER) mechanism at the gas-surface interface, the reaction between incident H/D atoms and pre-covered D/H atoms on Cu (111) has attracted much experimental and theoretical interest. Detailed final state-resolved experimental data have been available for about thirty-years, leading to the discovery of many interesting dynamical features. However, previous theoretical models have suffered from reduced-dimensional approximations and/or omitting energy transfer to surface phonons and electrons, or the high cost of on-the-fly molecular dynamics, preventing quantitative comparisons with experimental data. Herein, we report the first high-dimensional neural network potential (NNP) for this ER reaction based on first-principles calculations including all molecular and surface degrees of freedom. Thanks to the high efficiency of this NNP, we are able to perform extensive quasi-classical molecular dynamics simulations with the inclusion of the excitation of low-lying electron-hole pairs (EHPs), which generally yield good agreement with various experimental results. More importantly, the isotopic and/or EHP effects in total reaction cross-sections and distributions of the product energy, scattering angle, and individual ro-vibrational states have been more clearly shown and discussed. This study sheds valuable light on this important ER prototype and opens a new avenue for further investigations of ER reactions using various initial conditions, surface temperatures, and coverages in the future.
作为在气-固界面研究 Eley-Rideal（ER）机制的典型体系，入射 H/D 原子与 Cu（111）上预先覆盖的 D/H 原子之间的反应引起了广泛的实验和理论兴趣。大约三十年来，已经有详细的最终态分辨实验数据可用，这些数据揭示了许多有趣的动力学特征。然而，以前的理论模型受到降维近似和/或忽略向表面声子和电子的能量转移，或实时分子动力学的高成本的限制，无法与实验数据进行定量比较。在此，我们报告了基于第一性原理计算（包括所有分子和表面自由度）的这种 ER 反应的第一个高维神经网络势（NNP）。由于这个 NNP 的高效率，我们能够进行广泛的准经典分子动力学模拟，包括低能电子-空穴对（EHPs）的激发，这通常与各种实验结果吻合良好。更重要的是，总反应截面和产物能量、散射角以及各个转动振动态的分布中的同位素和/或 EHP 效应得到了更清楚的显示和讨论。这项研究为这个重要的 ER 原型提供了有价值的启示，并为未来使用各种初始条件、表面温度和覆盖度进一步研究 ER 反应开辟了新的途径。
