ISSN:
1057-9257
Keywords:
Neural network
;
Hyperpolarisabiiity
;
Dipole moment
;
Nitrobenzenes
;
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 netwrok is trained to predict the hyperpolarisability β of substituted nitrobenzenses as reported from EFISH experiments. Learning is faster with 13C NMR chemical shifts as input than with standard substituent constants and the predictions are somewhat better. The dipole moments μ can be predicted at the same time as β, but training to high precision is then much slower. Developments of this approach may be useful in screening out molecules of high β for synthesis and experimental study.
Additional Material:
5 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/amo.860040104
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