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Nitride has been drawing much attention due to its wide range of applicationsin optoelectronics and remains plenty of room for materials design anddiscovery. Here, a large set of nitrides have been designed, with their bandgap and alignment being studied by first-principles calculations combined withmachine learning. Band gap and band offset against wurtzite GaN accuratelycalculated by the combination of screened hybrid functional of HSE and DFT-PBEwere used to train and test machine learning models. After comparison amongdifferent techniques of machine learning, when elemental properties are takenas features, support vector regression (SVR) with radial kernel performs bestfor predicting both band gap and band offset with prediction root mean squareerror (RMSE) of 0.298 eV and 0.183 eV, respectively. The former is within HSEcalculation uncertainty and the latter is small enough to provide reliablepredictions. Additionally, when band gap calculated by DFT-PBE was added intothe feature space, band gap prediction RMSE decreases to 0.099 eV. Through afeature engineering algorithm, elemental feature space based band gapprediction RMSE further drops by around 0.005 eV and the relative importance ofelemental properties for band gap prediction was revealed. Finally, band gapand band offset of all designed nitrides were predicted and two trends werenoticed that as the number of cation types increases, band gap tends to narrowdown while band offset tends to go up. The predicted results will be a usefulguidance for precise investigation on nitride engineering.

Band gap and band alignment prediction of nitride based semiconductors using machine learning