Machine Learning with Semi-Empirical Quantum Mechanical Methods for Band Gap Prediction

Band gap predicted from the use of typical Density Functional Theory (DFT) functional are generally smaller than the true experimental gaps. Thus, there is a search for computational methods which can give accurate band gap predictions. There have been different studies which employed Machine Learning (ML) methods to predict the correct band gaps, or DFT combined with ML. In our work, we propose combining Machine Learning (ML) methods with semi-empirical quantum mechanical (SEQM) methods to predict the band gap of materials. Results using two SEQM parameterizations with ML will be presented. SEQM methods are usually computationally cheaper than DFT and, thus, our approach can achieve reasonable accuracy at only a fraction of the cost of DFT, DFT with ML, or DFT with hybrid functionals.