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

doi:10.1088/1674-1056/ab75d5

CORC > 金属研究所 > 中国科学院金属研究所

	Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*
	Zhu, Zhen 2; Dong, Baojuan 1,3,5; Guo, Huaihong 4; Yang, Teng 3; Zhang, Zhidong 3
刊名	CHINESE PHYSICS B
	2020-03-01
卷号	29 期号:4 页码:9
关键词	two-dimensional semiconductors machine learning
ISSN号	1674-1056
DOI	10.1088/1674-1056/ab75d5
通讯作者	Zhu, Zhen(zhuzhen@engineering.ucsb.edu) ; Yang, Teng(yangteng@imr.ac.cn)
英文摘要	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.
资助项目	National Key R&D Program of China[2017YFA0206301]
WOS研究方向	Physics
语种	英语
出版者	IOP PUBLISHING LTD
WOS记录号	WOS:000523403600001
资助机构	National Key R&D Program of China
内容类型	期刊论文
源URL	[http://ir.imr.ac.cn/handle/321006/137968]
专题	金属研究所_中国科学院金属研究所
通讯作者	Zhu, Zhen; Yang, Teng
作者单位	1.Shanxi Univ, Inst Optoelect, State Key Lab Quantum Opt & Quantum Opt Devices, Taiyuan 030006, Peoples R China 2.Univ Calif Santa Barbara, Dept Mat, Santa Barbara, CA 93106 USA 3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 4.Liaoning Shihua Univ, Coll Sci, Fushun 113001, Peoples R China 5.Shanxi Univ, Collaborat Innovat Ctr Extreme Opt, Taiyuan 030006, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Zhen,Dong, Baojuan,Guo, Huaihong,et al. Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*[J]. CHINESE PHYSICS B,2020,29(4):9.
APA	Zhu, Zhen,Dong, Baojuan,Guo, Huaihong,Yang, Teng,&Zhang, Zhidong.(2020).Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*.CHINESE PHYSICS B,29(4),9.
MLA	Zhu, Zhen,et al."Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*".CHINESE PHYSICS B 29.4(2020):9.