Abstract

The optimal method of the polymer Materials Informatics (MI) has not been developed because the amorphous nature of the higher-order structure affects these properties. We have now tried to develop the polymer MI’s descriptor of the higher-order structure using persistent homology as the topological method. We have experimentally studied the influence of the MD simulation cell size as the higher-order structure of the polymer on its electrical properties important for a soft material sensor or actuator device. The all-atom MD simulation of the polymer has been calculated and the obtained atomic coordinate has been analyzed by the persistent homology. The change in the higher-order structure by different cell size simulations affects the dielectric constant, although these changes are not described by a radial distribution function (RDF). On the other hand, using the 2nd order persistent diagram (PD), it was found that when the cell size is small, the island-shaped distribution become smoother as the cell size increased. There is the same tendency for the condition of change in the monomer ratio, the polymer chain length or temperature. As a result, the persistent homology may express the higher-order structure generated by the MD simulation as a descriptor of the polymer MI.

Details

Title
Higher-order structure of polymer melt described by persistent homology
Author
Shimizu Yohei 1 ; Kurokawa Takanori 2 ; Arai Hirokazu 2 ; Washizu Hitoshi 3 

 University of Hyogo, Graduate School of Simulation Studies, Kobe, Japan (GRID:grid.266453.0) (ISNI:0000 0001 0724 9317); JTEKT Corporation, Kashiwara, Japan (GRID:grid.471154.2) (ISNI:0000 0001 1544 0736) 
 JTEKT Corporation, Kashiwara, Japan (GRID:grid.471154.2) (ISNI:0000 0001 1544 0736) 
 University of Hyogo, Graduate School of Simulation Studies, Kobe, Japan (GRID:grid.266453.0) (ISNI:0000 0001 0724 9317); Kyoto University, Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto, Japan (GRID:grid.258799.8) (ISNI:0000 0004 0372 2033) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2480893559
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.