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Research Article

Neural Networks Evaluating NMR Data: An Approach To Visualize Similarities and Relationships of Sol−Gel Derived Inorganic−Organic and Organometallic Hybrid Polymers¹

Frank Hoehn, Ekkehard Lindner,* and Hermann A. Mayer*

Institut fr Anorganische Chemie der Universitt Tbingen, Auf der Morgenstelle 18, D-72076 Tbingen

Thomas Hermle and Wolfgang Rosenstiel*

Wilhelm-Schickard-Institut fr Informatik, Lehrstuhl fr Technische Informatik der Universitt Tbingen, Sand 13, D-72076 Tbingen

J. Chem. Inf. Comput. Sci., 2002, 42 (1), pp 36–45

DOI: 10.1021/ci010373z

Publication Date (Web): January 28, 2002

Corresponding authors: (E.L.) phone: +49-(0)7071-2972039; fax: +49-(0)7071-295306; e-mail: ekkehard.lindner@uni-tuebingen.de; (W.R.) phone: +49-(0)7071-2975482; fax: +49-(0)7071-295062; e-mail: rosenstiel@informatik.uni-tuebingen.de; (H.A.M.) phone: +49-(0)7071-2976229; e-mail: hermann.mayer@uni-tuebingen.de.

Abstract

An artificial neural network (ANN)the Kohonen Self-Organizing Feature Map (SOM)is used to evaluate solid-state NMR spectroscopic derived data of 72 siloxane-based phosphine and organometallic functionalized hybrid polymers. The data set consists of parameters that describe their structural features and their dynamic behavior. The ANN visualizes similarities of the investigated compounds by reducing the dimension of the data set. This allows a comparison of these polymers that was not possible beforehand because of their structural diversity.