The Investigation of Lattice Properties for Group-IV Sigesn Ternary Alloy: By Using Machine Learning Method

Abstract

Group-IV SiGeSn ternary alloy is a hot spot in the field of fabricating high-efficient Si-based light source due to its large lattice constant and bandgap variation range. However, due to the high cost and low speed of experimental and computational research, it is difficult to obtain their lattice constants comprehensively and quickly. Machine learning prediction based on statistics is an advanced method to solve this problem. In this paper, based on the existing data of group IV alloys, three machine learning methods such as Random Forest (RF), Support Vector Regression (SVR) and Gradient Boosting Decision Tree (GBDT) have been built to predict the lattice constants of SiGeSn. Firstly, the lattice constants of Group-IV alloys are collected to construct data set; Then, the data set are used to train the machine learning models which describe the quantitative relationship between concentrations and lattice constants; Finally, the prediction performance of these models are compared with each other, and the concentrations with appropriate lattice constants are predicted. The results show the comprehensive performance of SVR model is better than the other two, which means the SVR model can be used to directly predict the lattice constants of SiGeSn.