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

Abstract

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 screened hybrid functional of HSE 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 0.130 eV, respectively, both of which are within HSE calculation uncertainty. 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.

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Publication details

The article was received on 05 Nov 2018, accepted on 05 Feb 2019 and first published on 11 Feb 2019


Article type: Paper
DOI: 10.1039/C8TC05554H
Citation: J. Mater. Chem. C, 2019, Accepted Manuscript

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

    Y. Huang, C. Yu, W. Chen, Y. Liu, C. Li, C. Niu, F. Wang and Y. Jia, J. Mater. Chem. C, 2019, Accepted Manuscript , DOI: 10.1039/C8TC05554H

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