So, Changrok Kim, Young-Shin Park, Jong Hyuk Kim, Gwan-Yeong Cha, Daniel Ko, Jong Hwan Kang, Boseok 2024-01-12T02:32:27Z 2024-01-12T02:32:27Z 2023-01-27 2023-02 2640-4567 https://pubs.kist.re.kr/handle/201004/75813 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 efficient deep learning model to design acrylonitrile？butadiene？styrene (ABS)/CNT binary composites. A dataset of 14？945 data points is constructed from 110 studies. The results demonstrate that compared with a vanilla deep regression model, the proposed model achieves a 26% lower average mean absolute error and an accuracy of 80.6%, which is 16.2% higher than the vanilla deep regression model in predicting the six electrical and mechanical properties of target ABS/CNT composites. In addition, a Monte Carlo simulation integrated with the developed deep neural network (DNN) model effectively screens input variables for users and thus appropriately guides them to manufacture a composite product with desired physical properties. English Wiley Data-Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile-Butadiene-Styrene/Carbon Nanotube Binary Composites Article 10.1002/aisy.202200399 1 Advanced Intelligent Systems, v.5, no.2 Advanced Intelligent Systems 5 2 Y scie 000916619200001 Automation & Control Systems Computer Science, Artificial Intelligence Robotics Automation & Control Systems Computer Science Robotics Article WALLED CARBON NANOTUBES MECHANICAL-PROPERTIES GRAPHENE REINFORCEMENT DENSITY FILLERS acrylonitrile-butadiene-styrene carbon nanotubes deep learning deep neural network machine learning Monte Carlo simulation