Pattern Recognition for the Electronic Phase of Bismuth Antimony Thin Films

TL;DR

This paper develops pattern recognition tools, including SVM, decision tree, and neural networks, to accurately predict the electronic phase of bismuth antimony thin films, achieving up to 100% accuracy.

Contribution

It introduces machine learning models specifically designed for predicting the electronic phase of bismuth antimony thin films, addressing challenges due to their low symmetry and complex electronic properties.

Findings

Neural network achieved 100% prediction accuracy.

Decision tree achieved 95% accuracy.

Support vector machine achieved 90% accuracy.

Abstract

There are many applications involving the use of bismuth antimony thin films. However, due to the low crystalline symmetry and strong coupling between the electronic band edges, it has always been challenging to infer the electronic phase of such a material. Fortunately, with the development of pattern recognition technology, scientists can build many black-box tools for predicting various materials properties. In this present work, we have developed several pattern recognition tools to predict the electronic phase of a 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.