Mark K. Massey

Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. We show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve machine learning classification problems. This is the first time that EIM has been applied to such problems. We evaluate the approach on two standard datasets: Lenses and Iris. Comparing our technique with a well-known software-based evolutionary method indicates that EIM performs reasonably well. We suggest that EIM offers a promising new direction for evolutionary computation.

Computing with carbon nanotubes: Optimization of threshold logic gates using disordered nanotube/polymer composites

This paper explores the use of single-walled carbon nanotube (SWCNT)/poly(butyl methacrylate) composites as a material for use in unconventional computing. The mechanical and electrical properties of the materials are investigated. The resulting data reveal a correlation between the SWCNT concentration/viscosity/conductivity and the computational capability of the composite. The viscosity increases significantly with the addition of SWCNTs to the polymer, mechanically reinforcing the host material and changing the electrical properties of the composite. The electrical conduction is found to depend strongly on the nanotube concentration; Poole-Frenkel conduction appears to dominate the conductivity at very low concentrations (0.11% by weight). The viscosity and conductivity both show a threshold point around 1% SWCNT concentration; this value is shown to be related to the computational performance of the material. A simple optimization of threshold logic gates shows that satisfactory computation is only achieved above a SWCNT concentration of 1%. In addition, there is some evidence that further above this threshold the computational efficiency begins to decrease.

Exploring the alignment of carbon nanotubes dispersed in a liquid crystal matrix using coplanar electrodes

We report on the use of a liquid crystalline host medium to align single-walled carbon nanotubes in an electric field using an in-plane electrode configuration. Electron microscopy reveals that the nanotubes orient in the field with a resulting increase in the DC conductivity in the field direction. Current versus voltage measurements on the composite show a nonlinear behavior, which was modelled by using single-carrier space-charge injection. The possibility of manipulating the conductivity pathways in the same sample by applying the electrical field in different (in-plane) directions has also been demonstrated. Raman spectroscopy indicates that there is an interaction between the nanotubes and the host liquid crystal molecules that goes beyond that of simple physical mixing.

Evolution-in-materio: Solving function optimization problems using materials

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve computational problems. We demonstrate for the first time that this methodology can be applied to function optimization. We evaluate the approach on 23 function optimization benchmarks and in some cases results come very close to the global optimum or even surpass those provided by a well-known software-based evolutionary approach. This indicates that EIM has promise and further investigations would be fruitful.

Evolution-in-materio: Solving bin packing problems using materials

Evolution-in-materio (EIM) is a form of intrinsic evolution in which evolutionary algorithms are allowed to manipulate physical variables that are applied to materials. This method aims to configure materials so that they solve computational problems without requiring a detailed understanding of the properties of the materials. The concept gained attention through the work of Adrian Thompson who in 1996 showed that evolution could be used to design circuits in FPGAS that exploited the physical properties of the underlying silicon [21]. In this paper, we show that using a purpose-built hardware platform called Mecobo, we can solve computational problems by evolving voltages, signals and the way they are applied to a microelectrode array with a chamber containing single-walled carbon nanotubes and a polymer. Here we demonstrate for the first time that this methodology can be applied to the well-known computational problem of bin packing. Results on benchmark problems show that the technique can obtain results reasonably close to the known global optima. This suggests that EIM is a promising method for configuring materials to carry out useful computation.

Evolution-in-materio: A frequency classifier using materials

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we describe experiments using a purpose-built EIM platform called Mecobo to classify whether an applied square wave signal is above or below a user-defined threshold. This is the first demonstration that electrical configurations of materials (carbon nanotubes and a polymer) can be evolved to act as frequency classifiers.

Electrical Behavior of Langmuir–Blodgett Networks of Sorted Metallic and Semiconducting Single-Walled Carbon Nanotubes

Langmuir-Blodgett deposition has been used to form thin film networks of both metallic and semiconducting single-walled carbon nanotubes. These have been investigated to understand their physical, optical, and morphological properties. The electrical conductivities over the temperature range 80-350 K and across electrode gaps of 220 nm and 2 mm have been explored. In the case of semiconducting tubes, the results suggest that Poole-Frenkel conduction is the dominant electrical process at temperatures below 150 K and electric fields of greater than 1 MV m(-1). Metallic nanotube networks exhibit a decrease in resistance with a reduction in temperature. This can be approximated by a linear relationship, giving a temperature coefficient of resistance of 10(-3) K(-1).

Evolution-in-materio: solving computational problems using carbon nanotube---polymer composites

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.

Evolution of Electronic Circuits using Carbon Nanotube Composites

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material, using voltages as the external stimuli, into a form where a simple computational problem can be solved. The material consists of single-walled carbon nanotubes suspended in liquid crystal; the nanotubes act as a conductive network, with the liquid crystal providing a host medium to allow the conductive network to reorganise when voltages are applied. We show that the application of electric fields under computer control results in a significant change in the material morphology, favouring the solution to a classification task.

Alignment of liquid crystal/carbon nanotube dispersions for application in unconventional computing

We demonstrate the manipulation of single-walled carbon nanotube/liquid crystal composites using in-plane electric fields. The conductivity of the materials is shown to be dependant on the application of a DC bias across the electrodes. When the materials are subjected to this in-plane field, it is suggested that the liquid crystals orientate, thereby forcing the SWCNTs to follow in alignment. This process occurs over many seconds, since the SWCNTs are significantly larger in size than the liquid crystals. The opportunity for applying this material to unconventional computing problems is suggested.