Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consistin...

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Bibliographic Details
Main Authors: Takashi Kojima, Takashi Washio, Satoshi Hara, Masataka Koishi, Naoya Amino
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Polymers
Subjects:
filled rubber
microstructure
filler morphology
molecular dynamics simulations
machine learning
convolutional neural network
Online Access:https://www.mdpi.com/2073-4360/13/16/2683

Internet

https://www.mdpi.com/2073-4360/13/16/2683

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