  Nitride has been drawing much attention due to its wide range of applications
in optoelectronics and remains plenty of room for materials design and
discovery. Here, a large set of nitrides have been designed, with their band
gap and alignment being studied by first-principles calculations combined with
machine learning. Band gap and band offset against wurtzite GaN accurately
calculated by the combination of screened hybrid functional of HSE and DFT-PBE
were used to train and test machine learning models. After comparison among
different techniques of machine learning, when elemental properties are taken
as features, support vector regression (SVR) with radial kernel performs best
for predicting both band gap and band offset with prediction root mean square
error (RMSE) of 0.298 eV and 0.183 eV, respectively. The former is within HSE
calculation uncertainty and the latter is small enough to provide reliable
predictions. Additionally, when band gap calculated by DFT-PBE was added into
the feature space, band gap prediction RMSE decreases to 0.099 eV. Through a
feature engineering algorithm, elemental feature space based band gap
prediction RMSE further drops by around 0.005 eV and the relative importance of
elemental properties for band gap prediction was revealed. Finally, band gap
and band offset of all designed nitrides were predicted and two trends were
noticed that as the number of cation types increases, band gap tends to narrow
down while band offset tends to go up. The predicted results will be a useful
guidance for precise investigation on nitride engineering.
