Minamitani Emi, Shiga Takuma, Kashiwagi Makoto, Obayashi Ippei
Institute for Molecular Science, Okazaki 444-8585, Japan.
JST, PRESTO, Kawaguchi, Saitama 332-0012, Japan.
J Chem Phys. 2022 Jun 28;156(24):244502. doi: 10.1063/5.0093441.
Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a longstanding problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remains elusive. We solved this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. Using persistent homology, we constructed a topological descriptor that can predict thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features correlated with both the thermal conductivity and MRO. The results could provide an avenue for controlling material characteristics through the topology of the nanostructures.
量化无定形材料的复杂结构与其物理性质之间的相关性一直是材料科学中的一个长期问题。在非晶硅(一种典型的共价无定形固体)中,中程有序(MRO)的存在一直是人们深入讨论的话题。然而,与MRO相对应的具体原子排列及其与热导率等物理性质的关系仍然难以捉摸。我们通过结合拓扑数据分析、机器学习和分子动力学模拟解决了这个问题。利用持久同调,我们构建了一个可以预测热导率的拓扑描述符。此外,通过对描述符的逆分析,我们确定了与热导率和MRO都相关的典型环特征。这些结果可以为通过纳米结构的拓扑结构控制材料特性提供一条途径。