Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China.
J Chem Phys. 2022 Mar 7;156(9):094104. doi: 10.1063/5.0084545.
The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.
材料的表面在化学环境下经常经历剧烈的结构演变，这反过来又有助于确定材料的不同性质。在这里，我们开发了一种在巨正则系综中自动搜索最佳表面相（ASOP）的通用方法，该方法通过基于全局神经网络（G-NN）势的随机表面行走（SSW）全局优化来实现。ASOP 模拟首先枚举一系列组成网格，然后利用 SSW-NN 探索表面相的构型和组成空间，并依靠蒙特卡罗方案来关注能量有利的组成。该方法应用于催化乙烯环氧化条件下银表面氧化物的形成。重现了 Ag(111)上表面氧化物的已知相，并揭示了 Ag(100)上的新相，它们表现出新颖的结构特征，这对于理解乙烯环氧化可能至关重要。我们的结果表明，ASOP 方法为探索复杂表面结构提供了一种自动化和有效的方法，这有利于在工作条件下设计新型功能材料。
