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Ternary III-nitride-based nanowires with highly efficient light-emitting properties are essential for a broad range of applications. By using the selective area molecular-beam epitaxy method, InGaN/AlGaN quantum disks (QDs) embedded in hexagonal GaN nanowires were successfully grown. With the help of atomic-scale-resolved transmission electron microscopy and atom probe tomography, atomic ordering and other related structural information, such as crystallography and local chemistry, have been unambiguously revealed to provide unique insights into the exceptionally strong photoluminescence enhancements. A boomerang-shaped InGaN/AlGaN QD was identified, and atomic-level 1 : 1 periodic chemical ordering within the boomerang shaped AlGaN layers along the c -direction was revealed, confirming the preferential site occupation of Al-atoms. This type of growth provides a strong suppression of the quantum-confined Stark effect and is thus likely a very strong contributor to the exceptional properties. This work therefore enables us to directly establish the key structural elements necessary to understand the exceptionally strong emission exhibited by these materials. Optimization of the configurations of QDs could be an alternative design tool for developing various advanced LED device applications with well-designed structure and desirable optical properties.
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Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials
Abstract Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.
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- Award ID(s):
- 2011967
- PAR ID:
- 10506131
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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