Journal
SCRIPTA MATERIALIA
Volume 166, Issue -, Pages 117-121Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.scriptamat.2019.03.003
Keywords
Carbon nanotubes
Categories
Funding
- European Union's Horizon 2020 research and innovation program, under the Marie Sklodowska-Curie [642890]
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Industrial applications of conductive polymer composites with carbon nanotubes require precise tailoring of their electrical properties. While existing theoretical methods to predict the bulk conductivity require fitting to experiments and often employ power-laws valid only in the vicinity of the percolation threshold, the accuracy of numerical methods is accompanied with substantial computational efforts. In this paper we use recently developed physically-based finite element analyses to successfully train an artificial neural network to make predictions of the bulk conductivity of carbon nanotube-polymer composites at negligible computational cost. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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