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开发表面原子识别模型,对单个TiO_2颗粒升温烧结过程中表面原子进行分类研究。模型对颗粒空间立方体网格化,利用标准球形颗粒体积积分法确定最佳网格尺寸为0.3 nm。通过表面网格识别实现表面原子分类,采用近邻网格中外部网格数量(N_(ext))作为准则数判断目标网格是否为表面网格,确定最佳N_(ext)=9。基于LAMMPS软件模拟了半径为0.75 nm颗粒的升温过程,发现系统能量弛豫速度明显高于结构弛豫速度;利用表面识别模型分类分析原子特性,表面原子平均位移大于内部原子,且表面O原子迁移活性高于Ti原子;表面原子配位数低于内部原子,佐证表面结构规律性较差。研究结果为深入分析纳米材料活性位等结构分布奠定基础。
Development of surface atom recognition model, a single TiO 2 particles during the surface temperature sintering process classification. The model meshes the cube of the particle space, and the optimal grid size is 0.3 nm determined by the standard spherical particle volume integral method. The surface atomic classification is achieved by surface grid identification. The number of external grids (N_ (ext)) in the neighboring grids is used as the criterion to determine whether the target grids are surface grids and the optimal N ext = 9 is determined. Based on the LAMMPS software simulation of the temperature rising process of 0.75 nm particles, it is found that the system energy relaxation speed is significantly higher than that of the structure relaxation rate. By using the surface recognition model to classify and analyze the atomic properties, the average displacement of surface atoms is larger than that of internal atoms, Its activity is higher than that of Ti atoms. The coordination number of the surface atoms is lower than that of the internal atoms, which proves that the regularity of surface structure is poor. The results lay a foundation for in-depth analysis of the structural distribution of active sites in nanomaterials.