Hirai Hirotoshi, Jinnouchi Ryosuke
Toyota Central R&D Labs., Inc. 41-1, Yokomichi Nagakute Aichi 480-1192 Japan
RSC Adv. 2022 Aug 17;12(36):23274-23283. doi: 10.1039/d2ra04343b. eCollection 2022 Aug 16.
We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions. In this method, bias potentials promoting surface reactions are applied to enable statistical samplings of the surface reaction within the timescale of molecular dynamics (MD) simulations, and elementary reactions are extracted automatically using an extension of the method constructed for gas- or liquid-phase reactions. By converting the extracted elementary reaction data to directed graph data, MD trajectories can be efficiently mapped onto reaction pathways using a network analysis tool. To demonstrate the power of the method, it was applied to the steam reforming of methane on the Rh(111) surface and to propane reforming on the Pt(111) and PtSn(111) surfaces. We discover new energetically favorable pathways for both reactions and reproduce the experimentally-observed materials-dependence of the surface reaction activity and the selectivity for the propane reforming reactions.
我们提出了一种自动化方法，该方法无需实验数据且只需最少的人工干预就能绘制表面反应路径。在这种方法中，应用促进表面反应的偏置势，以便在分子动力学（MD）模拟的时间尺度内对表面反应进行统计采样，并使用为气相或液相反应构建的方法的扩展自动提取基元反应。通过将提取的基元反应数据转换为有向图数据，可以使用网络分析工具将MD轨迹有效地映射到反应路径上。为了证明该方法的强大功能，将其应用于Rh(111)表面上的甲烷蒸汽重整以及Pt(111)和PtSn(111)表面上的丙烷重整。我们发现了这两个反应新的能量有利路径，并重现了实验观察到的表面反应活性的材料依赖性以及丙烷重整反应的选择性。
