Li Chen, Li Yongle, Jiang Bin
Key Laboratory of Precision and Intelligent Chemistry, 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, International Center of Quantum and Molecular Structures, Shanghai Key Laboratory of High Temperature Superconductors, Shanghai University Shanghai 200444 China
Chem Sci. 2023 Apr 6;14(19):5087-5098. doi: 10.1039/d2sc06559b. eCollection 2023 May 17.
Elementary gas-surface processes are essential steps in heterogeneous catalysis. A predictive understanding of catalytic mechanisms remains challenging due largely to difficulties in accurately characterizing the kinetics of such steps. Experimentally, thermal rates for elementary surface reactions can now be measured using a novel velocity imaging technique, providing a stringent testing ground for rate theories. Here, we propose to combine ring polymer molecular dynamics (RPMD) rate theory with state-of-the-art first-principles-determined neural network potential to calculate surface reaction rates. Taking NO desorption from Pd(111) as an example, we show that the harmonic approximation and the neglect of lattice motion in the commonly-used transition state theory overestimates and underestimates the entropy change during the desorption process, respectively, leading to opposite errors in rate coefficient predictions and artificial error cancellations. Including anharmonicity and lattice motion, our results reveal a generally neglected surface entropy change due to significant local structural change during desorption and obtain the right answer for the right reasons. Although quantum effects are found to be less important in this system, the proposed approach establishes a more reliable theoretical benchmark for accurately predicting the kinetics of elementary gas-surface processes.
基本的气-固表面过程是多相催化中的关键步骤。由于难以准确表征这些步骤的动力学，对催化机理进行预测性理解仍然具有挑战性。在实验方面，现在可以使用一种新颖的速度成像技术来测量基本表面反应的热速率，为速率理论提供了一个严格的测试平台。在此，我们提议将环聚合物分子动力学（RPMD）速率理论与最先进的第一性原理确定的神经网络势相结合，以计算表面反应速率。以从Pd(111)表面解吸NO为例，我们表明，常用的过渡态理论中的谐波近似和对晶格运动的忽略，分别高估和低估了解吸过程中的熵变，导致速率系数预测出现相反的误差以及人为的误差抵消。考虑到非谐性和晶格运动，我们的结果揭示了由于解吸过程中显著的局部结构变化而普遍被忽视的表面熵变，并基于正确的原因得到了正确的答案。尽管在该体系中发现量子效应不太重要，但所提出的方法为准确预测基本气-固表面过程的动力学建立了一个更可靠的理论基准。
