Machine-learning-aided DFT-1/2 calculations for bandgaps of zinc oxide thin films

Wei Che Tseng, Chao Cheng Kaun, Yen Hsun Su

研究成果: Article同行評審


Using the Density Functional Theory (DFT) -1/2 method, the electronic structure and bandgap of zinc oxide (ZnO) are calculated, providing the initial data sets for machine learning based on the genetic-algorithm-based artificial neural networks (GANNs) model. The predicted bandgaps from the well-trained GANNs model are close to (less than 5%) the calculated ones from the DFT-1/2 method along cutoff radius = 2.4 for Oxygen and cutoff radius = 1.2 for Zinc, agreeing with the experimental data. Our results show that combining the DFT-1/2 method with GANNs is an efficient way to correct the band gap of Zinc Oxide in DFT simulation.

期刊Thin Solid Films
出版狀態Published - 2022 8月 1

All Science Journal Classification (ASJC) codes

  • 電子、光磁材料
  • 表面和介面
  • 表面、塗料和薄膜
  • 金屬和合金
  • 材料化學


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