Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Li, Jun; Luo, Kun; An, Qi

doi:10.1016/j.ijplas.2023.103552

Cited by 12 publications

(6 citation statements)

References 120 publications

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“…In our previous study, we demonstrated the accuracy of the ML-FF in describing the structural and mechanical properties of CdTe. 42 Here, to demonstrate that ML-FF is capable of characterizing the growth dynamics of CdTe homoepitaxial films, the PES for deposition and diffusion processes of Cd and Te atoms on the (111) Cd-and Te-terminated surfaces were calculated using ML-FF simulations and compared with those obtained from DFT simulations. Figure 2 3(c)).…”

Section: Resultsmentioning

confidence: 99%

“…All MD and tfMC simulations were carried out using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) 45 code, where the atomic interactions in CdTe were accurately represented by our validated ML-FF. 42 In this study, we focused on epitaxial growth of thick-layer CdTe films on single-crystal CdTe substrates. We explored different substrate polarities (Cd-or Te-terminated).…”

Section: Methodsmentioning

confidence: 99%

“…This approach allows the ML-FFbased MD simulations to achieve efficiencies comparable to that of MD simulations using empirical force fields, while also with great accuracy in energy and force predictions comparable to DFT simulations. 42 Our simulations provide a detailed view of the atomic-level mechanisms in the homoepitaxial growth of CdTe, especially highlighting the formation of lamella twins with (111) TBs parallel to the surface. We conducted systematic simulations under varying conditions, including different substrate polarities (Cd-or Te-terminated) and temperatures, to clarify trends in film quality and structure during CdTe homoepitaxial growth.…”

Section: Introductionmentioning

confidence: 95%

“…The atomic interactions of CdTe were accurately modeled using our recently developed machine-learning force field (ML-FF). 42 This ML-FF was developed by using deep neural networks to describe the atomic interactions of CdTe, with parameters finetuned via training on atomic configurations, forces, system energies, and virial stresses, all derived from density functional theory (DFT) simulations. This approach allows the ML-FFbased MD simulations to achieve efficiencies comparable to that of MD simulations using empirical force fields, while also with great accuracy in energy and force predictions comparable to DFT simulations.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we employed the combination of MD and time-stamped force-bias Monte Carlo (tfMC) simulations to examine the growth of CdTe films on the single-crystal CdTe substrate. The atomic interactions of CdTe were accurately modeled using our recently developed machine-learning force field (ML-FF) . This ML-FF was developed by using deep neural networks to describe the atomic interactions of CdTe, with parameters fine-tuned via training on atomic configurations, forces, system energies, and virial stresses, all derived from density functional theory (DFT) simulations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Elucidating Twinning Mechanisms in CdTe Homoepitaxial Film Growth from Machine Learning Force Field Molecular Dynamics Simulations

Li,

Luo,

2024

Crystal Growth & Design

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The process of twinning formation during the growth of semiconductor thin films, particularly in CdTe homoepitaxial films, is a complex phenomenon that has eluded comprehensive understanding. Here, this study investigated the growth dynamics of CdTe homoepitaxial films on (111) Cd-and Te-terminated surfaces across a range of substrate temperatures. We employed molecular dynamics and time-stamped force-bias Monte Carlo simulations with a machine-learning force field to explore these growth dynamics. Our findings reveal that CdTe homoepitaxial layers are characterized by lamella twins with (111) twin boundaries parallel to the surface. The development of stacking fault layers is potentially influenced by the presence of residual holes prior to the crystallization of subsequent layers. We also observed that (111) Te homoepitaxial layers exhibit more significant planar defects than (111) Cd homoepitaxial layers, and this disparity is more pronounced at lower substrate temperatures and can be attributed to the more reactive (111) Te-terminated surface. Moreover, there is a notable trend in which the prevalence of planar defects escalates with increasing substrate temperature, arising from the competing formation mechanism of CdTe zinc-blende and wurtzite structures. This work offers detailed atomiclevel insights into the growth and twinning mechanisms of CdTe homoepitaxial films.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Elucidating Twinning Mechanisms in CdTe Homoepitaxial Film Growth from Machine Learning Force Field Molecular Dynamics Simulations

Li,

Luo,

2024

Crystal Growth & Design

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Hyper‐Elastic Deformation via Martensitic Phase Transformation in Cadmium Telluride

Luo,

Han,

Cappola

et al. 2024

Adv Eng Mater

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Cadmium telluride (CdTe) is a highly promising material for photovoltaics and photodetectors due to its light‐absorbing properties. However, efficient design and use of flexible devices require a deep understanding of its atomic‐level deformation mechanism. Here, we investigate uniaxial compression deformation of CdTe monocrystalline with varying crystal orientations using molecular dynamics (MD) with a newly developed machine‐learning force field, alongside in‐situ micropillar compression experiments. Our findings reveal that CdTe bulk deformation is dominated by reversible martensitic phase transformation, whereas CdTe pillar deformation is primarily driven by dislocation nucleation and movement. CdTe monocrystals possess exceptional super‐recoverable deformation along the <100> orientation due to hyper‐elastic processes induced by martensitic transformation. This discovery not only sheds light on the peculiarities observed in micropillar experimental measurements but also provides pivotal insights into the fundamental deformation behaviors of CdTe and similar II‐VI compounds under various stress conditions. These insights are crucial for the innovative design and enhanced functionality of future flexible electronic devices.This article is protected by copyright. All rights reserved.

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Investigation of the CdZnTe (2 1 1) and (1 3 3) films grown on GaAs (2 1 1) controlled by temperature: Experiment and first-principles calculations

Liu,

Wu,

Zhang

et al. 2024

Applied Surface Science

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Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Cited by 12 publications

References 120 publications

Elucidating Twinning Mechanisms in CdTe Homoepitaxial Film Growth from Machine Learning Force Field Molecular Dynamics Simulations

Elucidating Twinning Mechanisms in CdTe Homoepitaxial Film Growth from Machine Learning Force Field Molecular Dynamics Simulations

Hyper‐Elastic Deformation via Martensitic Phase Transformation in Cadmium Telluride

Investigation of the CdZnTe (2 1 1) and (1 3 3) films grown on GaAs (2 1 1) controlled by temperature: Experiment and first-principles calculations

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