Artificial neural networks in Mössbauer material science

Mössbauer spectroscopy is a useful technique for characterizing the valencies, electronic and magnetic states, coordination symmetries and site occupancies of the cation. The Mössbauer parameters of isomer shift and quadrupole splitting are useful to distinguish paramagnetic ferrous and ferric iron in several substances, while the internal magnetic field provides information on the crystallinity. In recent years artificial neural networks have shown to be a powerful technique to solve problems of pattern recognition of a mineral from its Mössbauer spectrum, Mössbauer parameters data bank, crystalline structure and magnetic phases of soil from Mössbauer parameters. A computer software named Mössbauer Effect Assistant has been developed. It uses learning vector quantization neural network linked to a Mössbauer data bank that contains Mössbauer parameters of isomer shift, quadrupole spliting, internal magnetic field and the references of the substances. The program identifies the substance under study and/or its crystalline structure when fed with experimental Mössbauer parameters. It can also list the references from the literature by feeding the name of the substance or the author of the publication. Typical application of Mössbauer Effect Assistant in iron-bearing materials Mössbauer spectroscopy is present in user friendly Microsoft Windows environment.