Insight into the Electronic Properties of Semiconductor Heterostructure Based on Machine Learning and First-Principles

A first-principles approach is a powerful means of gaining insight into the intrinsic structure and properties of materials. However, with the implementation of material genetic engineering, it is still a challenging road to discover materials with high satisfaction. One alternative is to employ machine-learning techniques to mine data and predict performance. In this present contribution, the method is taken to predict the band gap opening value of graphene in a heterostructure. First, the data of 2076 binary compounds in the Materials Project library are used to achieve visual dimensionality reduction of the data set through a t-distributed stochastic neighbor embedding (t-SNE) algorithm in unsupervised learning. Then, a series of semiconductor components are screened out and form heterostructures with graphene. Second, by means of the ensemble learning EXtreme Gradient Boost (XGBoost) algorithm and support vector machine (SVM) technology, two prediction frameworks are built to predict the band gap opening value of the graphene in the system. Finally, density functional theory (DFT) is used to calculate the energy band and density of states for comparison. Analysis shows that the prediction model has an accuracy rate of 88.3%, and there is little difference between prediction results and calculation results. We anticipate that this framework model would have fascinating applications in predicting the electronic properties of various multiphase materials