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The Use of Computational Intelligence in the Design of Polymers and in Property Prediction

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Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2012)

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Abstract

Taking advantage of techniques from the field of Computational Intelligence, the goal of our research is to construct systems that can computationally design polymer optical fiber formulations with specified desirable consumer characteristics and to develop computational tools which can be used to rationalize and predict properties of polymeric materials, such as the glass transition temperature.

This research was supported by National Textile Center (NTC) grant C05-PH01 through U.S. Department of Commerce. Various aspects appeared in publications in Conference Proceedings and in the International Journal of Intelligent Systems.

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Authors and Affiliations

  1. Materials Science, Drexel University, Philadelphia, PA, 19104, USA

    Xi Chen

  2. Computer Science, Philadelphia University, Philadelphia, PA, 19144, USA

    Les M. Sztandera

  3. Chemistry Department, Oxford University, Oxford, OX1 3QZ, UK

    Hugh M. Cartwright & Stephen Granger-Bevan

Authors
  1. Xi Chen
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  2. Les M. Sztandera
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  3. Hugh M. Cartwright
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  4. Stephen Granger-Bevan
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Corresponding author

Correspondence to Xi Chen .

Editor information

Editors and Affiliations

  1. Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, Switzerland

    Gianni A. Di Caro

  2. CNRS UMR 5169, Research Center on Animal Cognition, Université Paul Sabatier, Toulouse, France

    Guy Theraulaz

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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chen, X., Sztandera, L.M., Cartwright, H.M., Granger-Bevan, S. (2014). The Use of Computational Intelligence in the Design of Polymers and in Property Prediction. In: Di Caro, G., Theraulaz, G. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-319-06944-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-06944-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06943-2

  • Online ISBN: 978-3-319-06944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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