ISSN:
1057-9257
Keywords:
Neural network
;
Polarisability
;
Aromatic hydrocarbon
;
Chemistry
;
Polymer and Materials Science
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Electrical Engineering, Measurement and Control Technology
,
Physics
Notes:
A standard back-propagation neural network is used to correct input semi-empirical molecular orbital calculations of polarisability tensors to fit experimental data for aromatic hydrocarbons. The method readily yields the correct component normal to the molecular plane but is restricted by a small training set. The network is also used to predict polarisability components for structures input as the pattern of rings fused to a central benzene ring. Semi-quantitative predictions are obtained depending on the size and method of presentation of the training set.
Additional Material:
1 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/amo.860020404
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