No abstract

Hexagonal boron nitride (hBN) and diamond are promising materials for next-generation electronics and optoelectronics. However, their combination is rarely reported. In this study, we for the first time demonstrate the success to direct growth of two-dimensional (2D) hBN crystal layers on diamond substrates by metalorganic vapor phase epitaxy. Compared with the disordered growth we found on diamond (100), atomic force microscopy, X-ray diffraction, and transmission electron microscopy results all support 2D hBN with highly oriented lattice formation on diamond (111). Also, the epitaxial relationship between hBN and diamond (111) substrate is revealed to be [0 0 0 1] hBN // [1 1 1] diamond and [1 0 1̅ 0] hBN // [1 1 2̅ ] diamond . The valence band offset at hBN/diamond (111) heterointerface determined by X-ray photoelectron spectroscopy is 1.4 ± 0.2 eV, thus yielding a conduction band offset of 1.0 ± 0.2 eV and type II staggered band alignment with a bandgap of 5.9 eV assumed for hBN. Furthermore, prior thermal cleaning of diamond in a pure H 2 atmosphere smoothens the surface for well-ordered layered hBN epitaxy, while thermal cleaning in a mixed H 2 and NH 3 atmosphere etches the diamond surface, creating many small faceted pits that destroy the following epitaxy of hBN.

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, 2 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.