Computational neural networks:
a general purpose tool
for nanotechnology
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(a)Department of Chemistry, Colorado State
College
(b) Department of Chemistry, University of Arkansas,
Little Rock
(c) Oak Ridge National Laboratory
email: [email protected]
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This is an abstract
for a poster to be presented at the
Fifth
Foresight Conference on Molecular Nanotechnology.
There will be a link from here to the full article when it is
available on the web.
A computational scheme which utilizes neural networks was
developed to predict properties of nano-structured materials and
optimization and control of nano-devices. Using a set of simple
algorithms to encode the structure and composition of the
material directly into numerical vectors neural network modules
can correlate these numeric inputs with a set of desired
properties. Calculated results for a series of hydrocarbons,
fluorohydrocarbons, amines, and crown ethers demonstrate average
accuracies of 0.2-8.1% with maximum deviations of 16-20% for a
broad range of thermodynamic, physical, biological (toxicity:
human and environmental) and physical-chemical characteristics
(heat capacity, enthalpy, heat of evaporation, boiling point,
density, refractive index, stability constants, etc.). A
molecular design tool based on the neural network capabilities of
formulating accurate quantitative structure-property
relationships is described. This technique, called computational
synthesis, is capable of formulating the structure and
composition of materials which will give a set of specified
properties. In other applications, this technique has been proven
useful in the reverse engineering of nano-fluidics and
nano-motors.
Research sponsored by the Division of Materials Sciences,
Office of Basic Energy Sciences, U.S. Department of Energy under
contract DE-AC05-96OR22464 with Lockheed-Martin Energy Research
Corp.
*Corresponding Address:
Sally A. Meyer, Oak Ridge National Laboratory, Chemical and
Analytical Sciences Division
P. O. Box 2008, Oak Ridge, TN 37831-6197
ph: (423) 574-4974, fax: (423) 576-5235, email: [email protected]
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