|(a)Department of Chemistry, Colorado State
(b) Department of Chemistry, University of Arkansas, Little Rock
(c) Oak Ridge National Laboratory
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.
Sally A. Meyer, Oak Ridge National Laboratory, Chemical and Analytical Sciences Division
P. O. Box 2008, Oak Ridge, TN 37831-6197
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