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Chin. Phys. B, 2020, Vol. 29(4): 046101    DOI: 10.1088/1674-1056/ab75d5

Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning

Zhen Zhu(朱震)1, Baojuan Dong(董宝娟)2,4,5, Huaihong Guo(郭怀红)3, Teng Yang(杨腾)2, Zhidong Zhang(张志东)2
1 Materials Department, University of California, Santa Barbara, CA 93106, USA;
2 Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China;
3 College of Sciences, Liaoning Shihua University, Fushun 113001, China;
4 State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan 030006, China;
5 Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
Abstract  Two-dimensional (2D) semiconductors isoelectronic to phosphorene have been drawing much attention recently due to their promising applications for next-generation (opt)electronics. This family of 2D materials contains more than 400 members, including (a) elemental group-V materials, (b) binary III-VII and IV-VI compounds, (c) ternary III-VI-VII and IV-V-VII compounds, making materials design with targeted functionality unprecedentedly rich and extremely challenging. To shed light on rational functionality design with this family of materials, we systemically explore their fundamental band gaps and alignments using hybrid density functional theory (DFT) in combination with machine learning. First, calculations are performed using both the Perdew-Burke-Ernzerhof exchange-correlation functional within the general-gradient-density approximation (GGA-PBE) and Heyd-Scuseria-Ernzerhof hybrid functional (HSE) as a reference. We find this family of materials share similar crystalline structures, but possess largely distributed band-gap values ranging approximately from 0 eV to 8 eV. Then, we apply machine learning methods, including linear regression (LR), random forest regression (RFR), and support vector machine regression (SVR), to build models for the prediction of electronic properties. Among these models, SVR is found to have the best performance, yielding the root mean square error (RMSE) less than 0.15 eV for the predicted band gaps, valence-band maximums (VBMs), and conduction-band minimums (CBMs) when both PBE results and elemental information are used as features. Thus, we demonstrate that the machine learning models are universally suitable for screening 2D isoelectronic systems with targeted functionality, and especially valuable for the design of alloys and heterogeneous systems.
Keywords:  two-dimensional semiconductors      machine learning  
Received:  31 December 2019      Revised:  31 January 2020      Published:  05 April 2020
PACS:  73.61.Cw (Elemental semiconductors)  
  61.46.-w (Structure of nanoscale materials)  
  73.22.-f (Electronic structure of nanoscale materials and related systems)  
Fund: This work is dedicated to Michelle Mucheng Zhu. Project supported by the National Key R&D Program of China (Grant No. 2017YFA0206301).
Corresponding Authors:  Zhen Zhu, Teng Yang     E-mail:;

Cite this article: 

Zhen Zhu(朱震), Baojuan Dong(董宝娟), Huaihong Guo(郭怀红), Teng Yang(杨腾), Zhidong Zhang(张志东) Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning 2020 Chin. Phys. B 29 046101

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