ARTIFICIAL NEURAL NETWORKS IN MOSSBAUER MATERIAL SCIENCE

Mossbauer spectroscopy is a useful technique for characterizing the va lencies, electronic and magnetic states, coordination symmetries and s ite occupancies of the cation. The Mossbauer parameters of isomer shif t and quadrupole splitting are useful to distinguish paramagnetic ferr ous and ferric iron in several substances, while the internal magnetic field provides information on the crystallinity. In recent years arti ficial neural networks have shown to be a powerful technique to solve problems of pattern recognition of a mineral from its Mossbauer. spect rum, Mossbauer parameters data bank, crystalline structure and magneti c phases of soil from Mossbauer parameters. A computer software named Mossbauer Effect Assistant has been developed. It uses learning vector quantization neural network linked to a Mossbauer data bank that cont ains Mossbauer parameters of isomer shift, quadrupole spliting, intern al magnetic field and the references of the substances. The program id entifies the substance under study and/or its crystalline structure wh en fed with experimental Mossbauer parameters. It can also list the re ferences from the literature by feeding the name of the substance or t he author of the publication. Typical application of Mossbauer Effect Assistant in iron-bearing materials Mossbauer spectroscopy is present in user friendly Microsoft Windows environment.