Abstract
The general aim of the NASCENCE project is to model, understand and exploit the behaviour
of evolving nanosystems, with the long term goal to build information processing devices
exploiting these architectures without reproducing individual components. The project is
inspired by the success of natural evolution and the advancement of nanotechnology, and the
expectation that we soon reach the limits of miniaturisation in digital circuitry (Moore's Law).
The basic idea of NASCENCE is to use computer-controlled manipulation of physical
systems composed of nanoscale self-assembled components to steer them towards doing
useful computation and information processing. The project aims at the physical realization
and theoretical characterization of such programmable and (re)configurable network
architectures. Using computer-controlled manipulation and evolution, e.g. by genetic
algorithms, such network architectures are trained via external stimuli to perform a
computational task or information processing function. Basically, we shall utilize a block of
matter that allows us to change its properties/behaviour by external stimuli. These external
forces induce property changes, which we can control by algorithms that run on a digital
computer, so that there is a direct link between the external forces that we have control over
and the induced changes in the block of matter. Of course we cannot directly program the
particle dynamics and we do not have control of the particles, at least not directly. The
particles will interact with their nearest neighbours and we can exploit these phenomena
along with the state changes in regions of the block of matter, in order to perform
computations. In its broadest sense, it is a physical device whose physical state or
configuration is determined by discrete sets of signals, e.g. voltages, currents or magnetic
fields. The idea is that a digital computer can supply, collect and optimize the configuration
data that may be transformed into another physical data format, e.g. strings of zeros and ones.
A form of Darwinian evolution can be used to reward good candidate configurations,
recombine them into new candidate configurations, and converge to configurations that carry
out the desired computation.
Expected main outputs of the NASCENCE project focus on:
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developing evolving nanosystems based on new materials, moving from existing
proofs-of-concept on a centimetre scale and simulated systems on a micrometre scale
to systems on a scale of 10-100 nanometres
developing a generic interface consisting of hardware and software for testing,
exploring and evolving such systems, to be used within and beyond the time frame of
the project
developing a novel theoretical foundation for such systems based on discrete
mathematical concepts, in particular on graph theoretical concepts, extending the
theory in these areas as well
developing novel simulation software and tools based on the newly developed
theoretical foundations, moving from time consuming methods based on differential
equations to faster methods that will be suitable for very large scale simulations
Benefits from the project will be in the form of new computational strategies and algorithms,
new nanostructured materials, and probing and exploiting new concepts in tuning charge
transport and storage in new thin film architectures. Increased understanding of the interplay
between structural and electronic processes in these devices will create new paradigms in
computation and materials design and open up a range of possibilities for fabrication of a new
generation of electronic devices.
During the first stages of the project, members of NASCENCE have designed and
constructed a purpose-built hardware interface together with an API and new software to
enable all team members to use a conventional digital computer to interact with the physical
systems that have been developed by other members of NASCENCE, with the purpose to
evolve them towards doing useful computation. First experiments have taken place with
different types of material samples composed of carbon nanotubes and gold nanoparticles,
respectively, in combination with other compounds. The results look promising, with a
variety of computational tasks being evolved successfully, ranging from universal logic gates
and small circuits to combinatorial problem solving. We have also developed a
mathematical/physical model for the self-assembled nanoparticle networks we use, and we
have built a simulation tool based on this model. We have also started to apply explorative
techniques based on neural networks to model, simulate and explore our samples.These are
all first steps towards our target to lay the technological and theoretical foundations for this
new kind of information processing technology.
During the presentation we will give more details on the progress.