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Supervised Self-Organizing Maps in Crystal Property and Structure Prediction |
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AbstractSupervised self-organizing maps (SOMs) provide a new method to explore large numbers of crystal structures and to visualize structureproperty relationships. Examples show how powder diffraction patterns and one or more structural properties determine the positions of crystal structures on the supervised SOMs, and show that the maps allow the classification of structures, prediction of properties, and subset selection in polymorph prediction.
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