Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites

Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites

The field of polymer‐based nanoscience has always been of significant interest in the search for polymer/carbon nanotube (CNT) nanocomposites with optimized material properties for new applications. Herein, it is demonstrated that data collected from the online literature can be used to develop an e...

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Bibliographic Details
Main Authors: Changrok So, Young-Shin Kim, Jong Hyuk Park, Gwan-Yeong Kim, Daniel Cha, Jong Hwan Ko, Boseok Kang
Format: Article
Language:English
Published: Wiley 2023-02-01
Series:Advanced Intelligent Systems
Subjects:
acrylonitrile–butadiene–styrene
carbon nanotubes
deep learning
deep neural network
machine learning
Monte Carlo simulation
Online Access:https://doi.org/10.1002/aisy.202200399

Internet

https://doi.org/10.1002/aisy.202200399

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