A first principles investigation of ternary and quaternary II–VI zincblende semiconductor alloys

Mannodi-Kanakkithodi, Arun

doi:10.1088/1361-651x/ac59d8

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…Electronic terms are not included explicitly. 19 Apart from the energy minimization, the Monte Carlo simulations can be run in a matter of minutes. These run times would be very similar when simulating other compositions.…”

Section: Discussionmentioning

confidence: 99%

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A single crystal diffuse scattering study of structural relaxations arising from dopants in the semiconductor Cd0.9Zn0.1Te

Gutmann,

Kopach,

Kopach

et al. 2024

Journal of Applied Physics

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We have measured diffuse scattering in a single crystal of Cd0.9Zn0.1Te using a state-of-the-art laboratory diffractometer. A large-box atomistic simulation of a model crystal is used in conjunction with Monte Carlo modeling and the Kirkwood potential. A combination of structural relaxation in the presence of the dopant and thermal motion results in good qualitative agreement between the computed diffraction patterns of the model crystal and the measured x-ray patterns. This is shown to be rather distinct from the diffuse scattering arising from purely structural relaxations or thermal motion only. The atoms are shown to displace predominantly in ⟨1,1,1⟩ and ⟨1,0,0⟩ type directions. Our approach to Monte Carlo modeling can easily be extended to more complex defect structures to incorporate, e.g., chemical ordering on the Cd/Zn sublattice, more than one species of dopant or vacancies.

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Section: Discussionmentioning

confidence: 99%

“…It is hoped that the box of atoms obtained can be used to compute electronic properties such as the bandgap to further compare and optimize device performance. 19…”

Section: Discussionmentioning

confidence: 99%

A single crystal diffuse scattering study of structural relaxations arising from dopants in the semiconductor Cd0.9Zn0.1Te

Gutmann,

Kopach,

Kopach

et al. 2024

Journal of Applied Physics

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show abstract

“…45,46 The specific choice of the 32% mixing parameter (or α = 0.32) is motivated by studies in the literature 47 and by the fact that it closely reproduces the experimental bandgaps of CdTe (1.47 eV vs the experimental value of 1.5 eV) and CdSe (1.64 eV vs the experimental value of 1.7 eV). 12,48 Computed DFEs and CTLs from PBE and HSE + SOC are plotted against each other in Fig. 3.…”

Section: Articlementioning

confidence: 99%

Accelerating defect predictions in semiconductors using graph neural networks

Rahman,

Gollapalli,

Manganaris

et al. 2024

APL Machine Learning

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First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

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Nowotny-Juza phase KBeX (X = N, P, As, Sb, and Bi) half-Heusler compounds: applicability in photovoltaics and thermoelectric generators

et al. 2022

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A first principles investigation of ternary and quaternary II–VI zincblende semiconductor alloys

Cited by 5 publications

References 54 publications

A single crystal diffuse scattering study of structural relaxations arising from dopants in the semiconductor Cd0.9Zn0.1Te

A single crystal diffuse scattering study of structural relaxations arising from dopants in the semiconductor Cd0.9Zn0.1Te

Accelerating defect predictions in semiconductors using graph neural networks

Nowotny-Juza phase KBeX (X = N, P, As, Sb, and Bi) half-Heusler compounds: applicability in photovoltaics and thermoelectric generators

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