scispace - formally typeset
Y

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
More filters
Journal ArticleDOI

Electronic and excitonic properties of two-dimensional and bulk InN crystals

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).
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

Neural network potential for studying the thermal conductivity of Sn

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).