Amamoto Yoshifumi, Kojio Ken, Takahara Atsushi, Masubuchi Yuichi, Ohnishi Takaaki
Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Department of Materials Physics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
Patterns (N Y). 2020 Oct 28;1(8):100135. doi: 10.1016/j.patter.2020.100135. eCollection 2020 Nov 13.
The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties. It was determined that a unified parameter with topological and spatial information universally describes some parameters related to the stresses. This approach enables us to uncover the role of individual crosslinking points for the stresses, even in complicated structures. Based on the data science, we anticipate that the structure-property relationships of heterogeneous materials can be interpretatively represented using this type of "white box" approach.
材料复杂的结构-性能关系最近已使用数据科学方法进行描述,该方法被认为是材料科学中的第四范式。在网络聚合物或弹性体中,交联点之间聚合物链的连接方式对材料性能有显著影响。在本研究中,我们基于数据科学中使用的方法之一——复杂网络,在介观尺度上定量评估弹性体的结构异质性,以描述其弹性性能。结果确定,一个包含拓扑和空间信息的统一参数可以普遍描述一些与应力相关的参数。这种方法使我们能够揭示即使在复杂结构中单个交联点对应力的作用。基于数据科学,我们预计可以使用这种“白盒”方法解释性地表示异质材料的结构-性能关系。