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Centre for Molecular and Nanoscale Electronics
Abstract network molecules

Computational Carbon

Living systems are able to achieve remarkable feats of computation.

Massey MK, Kotsialos A, Volpati D, Vissol-Gaudin E, Pearson C, Bowen L, Obara B, Zeze DA, Groves C, Petty MC 2016 Evolution of Electronic Circuits using Carbon Nanotube Composites – Scientific Reports 6:32197

Living systems are able to achieve remarkable feats of computation (e.g. object recognition, decision making, reasoning) with both speed and efficiency. Many of these tasks have not yet been adequately solved using algorithms running on our most powerful computers. The Computational Carbon project combines the skills of Materials Scientists, Computer Scientists and Electronic Engineers within the School of Engineering and Computing Sciences at Durham with those of colleagues at the University of York, The University of Twente and the Norwegian University for Science and Technology. An initial aim is to model and understand the behaviour of molecular networks (e.g. arrays of carbon nanotubes, as shown left). However, a long term goal is to build an electronic processor exploiting these architectures without reproducing individual component function - a computer without transistors!
In the early stages, the work will be complementary to silicon-based electronics, providing a means to solve problems which are better addressed by parallel computation (e.g. pattern recognition). Knowledge will be gained about new forms of computation and how to control complex systems. In the longer term, the project could lead to fundamental changes to the way in which we build and operate computers.

  1. C. Venet, C Pearson, A S Jombert, M F Mabrook, D A Zeze and M C Petty, The morphology and electrical conductivity of single-wall carbon nanotubes thin films prepared by the Langmuir-Blodgett technique, Colloids and Surfaces A, 354 (2010) 113-117.
  2. M K Massey, C Pearson, D A Zeze, B G Mendis and M C Petty, The electrical and optical properties of oriented Langmuir-Blodgett films of single-walled carbon nanotubes, Carbon, 49 (2011) 2424.
  3. Vissol-Gaudin E, Kotsialos A, Massey MK, Zeze DA, Pearson C, Groves C, Petty MC Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms Parallel Problem Solving from Nature PPSN XIV Volume 9921 of the series Lecture Notes in Computer Science 644-654. 2016
  4. Vissol-Gaudin E, Kotsialos A, Massey MK, Zeze DA, Pearson C, Groves C, Petty MC. Training a Carbon Nanotube Liquid Crystal Classifier Using Evolutionary Algorithms – Unconventional Computation and Natural Computation 9726:130-141, 2016

Contact: Mike Petty (m.c.petty@durham.ac.uk) for more details.

Computational Carbon

Living systems are able to achieve remarkable feats of computation

Massey MK, Kotsialos A, Volpati D, Vissol-Gaudin E, Pearson C, Bowen L, Obara B, Zeze DA, Groves C, Petty MC 2016 Evolution of Electronic Circuits using Carbon Nanotube Composites – Scientific Reports 6:32197

Living systems are able to achieve remarkable feats of computation (e.g. object recognition, decision making, reasoning) with both speed and efficiency. Many of these tasks have not yet been adequately solved using algorithms running on our most powerful computers. The Computational Carbon project combines the skills of Materials Scientists, Computer Scientists and Electronic Engineers within the School of Engineering and Computing Sciences at Durham with those of colleagues at the University of York, The University of Twente and the Norwegian University for Science and Technology. An initial aim is to model and understand the behaviour of molecular networks (e.g. arrays of carbon nanotubes, as shown left). However, a long term goal is to build an electronic processor exploiting these architectures without reproducing individual component function - a computer without transistors!

In the early stages, the work will be complementary to silicon-based electronics, providing a means to solve problems which are better addressed by parallel computation (e.g. pattern recognition). Knowledge will be gained about new forms of computation and how to control complex systems. In the longer term, the project could lead to fundamental changes to the way in which we build and operate computers.

  1. C. Venet, C Pearson, A S Jombert, M F Mabrook, D A Zeze and M C Petty, The morphology and electrical conductivity of single-wall carbon nanotubes thin films prepared by the Langmuir-Blodgett technique, Colloids and Surfaces A, 354 (2010) 113-117.
  2. M K Massey, C Pearson, D A Zeze, B G Mendis and M C Petty, The electrical and optical properties of oriented Langmuir-Blodgett films of single-walled carbon nanotubes, Carbon, 49 (2011) 2424.
  3. Vissol-Gaudin E, Kotsialos A, Massey MK, Zeze DA, Pearson C, Groves C, Petty MC Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms Parallel Problem Solving from Nature PPSN XIV Volume 9921 of the series Lecture Notes in Computer Science 644-654. 2016
  4. Vissol-Gaudin E, Kotsialos A, Massey MK, Zeze DA, Pearson C, Groves C, Petty MC. Training a Carbon Nanotube Liquid Crystal Classifier Using Evolutionary Algorithms – Unconventional Computation and Natural Computation 9726:130-141, 2016

Contact: Mike Petty (m.c.petty@durham.ac.uk) for more details.