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There are many applications involving the use of bismuth antimony thin films.However, due to the low crystalline symmetry and strong coupling between theelectronic band edges, it has always been challenging to infer the electronicphase of such a material. Fortunately, with the development of patternrecognition technology, scientists can build many black-box tools forpredicting various materials properties. In this present work, we havedeveloped several pattern recognition tools to predict the electronic phase ofa bismuth antimony thin film. The support vector machine, the decision tree,and the artificial neural network are used to achieve a prediction accuracy of~90%, ~95% and ~100%, respectively.

Pattern Recognition for the Electronic Phase of Bismuth Antimony Thin Films