New materials band gap prediction based on the high-throughput calculation and the machine learning

Abstract

The bandgap often plays an important role in functional materials applications. For example, optoelectronic materials are generally wide bandgap semiconductors, while thermoelectric materials are narrow bandgap semiconductor materials. Therefore, predicting the bandgap rapidly and accurately for a given class of materials structures has great scientific importance for the functional materials applications. However, considering that the method of obtaining high-precision band gaps based on first-principles high-throughput calculations is time consuming and inefficient, and it is also not realistic to systematically measure a large number of material system band gaps. Machine learning methods based the statistics may be a promising alternative. This paper designs an ensemble learning model for effectively and accurately predicting bandgap values. Based on the calculated band gap values of diamond-like structures in thermoelectric materials, on the one hand, single component substitution strategy was used to generate large quantities of similar compounds, and the repetitive structures was filtered out by using the structural repeatability examination technique, resulting in 356 unique material structures. On the other hand, in combination with machine learning techniques, an efficient band gap prediction model was constructed, and by which the band gap values ​​of 50 similar material systems are predicted and verified. As is the result of the experiment, this prediction model has 77.73% accuracy. It is enough robustness and stability to be widely used in thermoelectric materials application scenarios which require large band gap prediction.