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arXiv 1708.04766cond-mat.mes-hall
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基于机器学习的二维半导体基本带隙和取向研究

Fundamental Band Gap and Alignment of Two-Dimensional Semiconductors Explored by Machine Learning

Zhen Zhu, Baojuan Dong, Teng Yang, Zhi-Dong Zhang

摘要:

与磷烯等电子的二维半导体材料因其在下一代电子学中的应用前景而备受关注。这个二维材料家族包含400多个成员,包括(a)元素V族材料,(b)二元III-VII和IV-VI化合物,(c)三元III-VI-VII和IV-V-VII化合物,使具有目标功能的材料设计前所未有地丰富和极具挑战性。为了阐明这一系列材料的合理功能设计,我们系统地探索了它们的基本带隙和对齐,使用混合密度泛函理论(DFT)结合机器学习。首先,进行GGA-PBE和HSE计算作为参考。我们发现这一系列的材料具有相似的晶体结构,但具有很大程度上分布的带隙值范围约为0至8 eV。然后,我们应用机器学习方法,包括线性回归(LR),随机森林回归(RFR)和支持向量机回归(SVR),来建立预测电子特性的模型。在这些模型中,SVR被认为具有最好的性能,产生的均方根误差(RMSE)小于0.15 eV的预测带隙,VBMs,和CBMs时,PBE的结果和元素信息被用作功能。因此,我们证明了机器学习模型普遍适用于筛选具有目标功能的二维等电子系统,对于合金和非均相系统的设计特别有价值。

英文摘要:

Two-dimensional (2D) semiconductors isoelectronic to phosphorene has 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, GGA-PBE and HSE calculations are performed as a reference. We find this family of materials share similar crystalline structures, but possess largely distributed band-gap values ranging approximately from 0 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 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 predicted band gaps, VBMs, and CBMs when both PBE results and elemental information are used as features. Thus, we demonstrate 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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