2021 Fiscal Year Final Research Report

Material search based on complex network analysis

Planned Research

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Project Area	Discrete Geometric Analysis for Materials Design
Project/Area Number	17H06468
Research Category	Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
Allocation Type	Single-year Grants
Review Section	Science and Engineering
Research Institution	Rikkyo University (2019-2021) The University of Tokyo (2017-2018)
Principal Investigator	Ohnishi Takaaki 立教大学, 人工知能科学研究科, 教授 (10376387)
Co-Investigator(Kenkyū-buntansha)	高橋数冴東京大学, 医科学研究所, 助教 (00909385) 久野遼平東京大学, 大学院情報理工学系研究科, 講師 (60725018)
Project Period (FY)	2017-06-30 – 2022-03-31
Keywords	高分子ブロック共重合体 / 相分離構造 / エラストマー / 中心性 / 架橋 / マルチフラクタル / 複雑ネットワーク / 化合物空間
Outline of Final Research Achievements	We quantitatively evaluated the structural heterogeneity of elastomers based on complex network 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. By analyzing the weighted network structure of elastomers, we found that how the centrality of each node changes during elongation was significantly affected by the concentration under which crosslinking reaction was carried out. We attempted to quantify the shape of various metastable morphologies of microphase separation in block copolymers that can be observed in a computer simulation. We showed that the size of the largest connected component and the indicators relating to the multifractal analysis were strongly correlated with the free energy of the metastable structures.
Free Research Field	ビッグデータ解析
Academic Significance and Societal Importance of the Research Achievements	複雑ネットワーク科学を応用してエラストマーの架橋点の空間上の位置とネットワーク上の位置の両方を考慮した新たな中心性指標を開発したことで，応力の統一的記述が可能になり，架橋点の応力への寄与や応力が集中する分子鎖の推定を実現した．ミクロ相分離の複雑な構造をマルチフラクタルで特徴づける手法を開発し，特異性指数や一般化次元などのマルチフラクタル指標が力学物性や自由エネルギーに関係していることを見出した．ミクロ相分離の構造情報のみから応力特性や自由エネルギーをある程度予測することが可能となり，計算コストの高いブロックコポリマーのシミュレーションにおけるパラメータ探索の効率化につながった．