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Yingjie Chen
Researcher at Beijing University of Posts and Telecommunications
Publications - 14
Citations - 84
Yingjie Chen is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Heterojunction & van der Waals force. The author has an hindex of 3, co-authored 6 publications receiving 40 citations.
Papers
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Electronic and excitonic properties of two-dimensional and bulk InN crystals
Dan Liang,Ruge Quhe,Yingjie Chen,Liyuan Wu,Qian Wang,Pengfei Guan,Shumin Wang,Shumin Wang,Pengfei Lu,Pengfei Lu +9 more
TL;DR: In this paper, the structural and optoelectronic properties of two-dimensional (2D) InN as well as its threedimensional (3D) counterparts were calculated by using density functional theory (DFT).
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Design and analysis of III-V two-dimensional van der Waals heterostructures for ultra-thin solar cells
TL;DR: In this paper , van der Waals (vdW) heterostructures consist of six group III-V monolayers (M = Ga, In, X = P, As, Sb) have been systematically investigated via hybrid functional HSE06 based on DFT calculations in order to evaluate their utilities in potential solar cell materials.
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Closing the bandgap for III-V nitrides toward mid-infrared and THz applications
TL;DR: Electrical properties of InNBi alloy enable III-nitride closing the bandgap and make this material a good candidate for future photonic device applications in the mid-infrared to THz energy regime.
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Type‐II van der Waals Heterostructures Based on AsP and Transition Metal Dichalcogenides: Great Promise for Applications in Solar Cell
TL;DR: In this article , a stable vdW heterostructures based on arsenic phosphorus (AsP) and transition metal dichalcogenides are designed and the geometry, electronic, and optical properties for type-II AsP/MX2 (Mo, W, X, S, Se) by first-principle calculations are systematically explored and their application in solar cell materials is predicted.
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Neural network potential for studying the thermal conductivity of Sn
Lihong Han,Xingrun Chen,Qian Wang,Qian Wang,Yingjie Chen,Mingfei Xu,Liyuan Wu,Changcheng Chen,Pengfei Lu,Pengfei Guan +9 more
TL;DR: In this paper, the authors used a machine learning method of the atomic energy network (aenet) to construct the potential function of Sn based on the 11,516 training sets of density functional theory (DFT).