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density-functional-theory (DFT) accurate large scale atomistic simulations of defects including dislocations, grain boundaries and precipitates, as well as phase diagrams exploration. A key challenge faced
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(DFT) and Energy Decomposition Analysis will be applied to model dissociation processes and reaction mechanisms, allowing for direct comparison with experimental data. Kinetic modeling using RRKM theory
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applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such in machine learning
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, HNMR, BET etc.) will be employed to characterize the carbon nanofiber structures. For molecular level, understanding, DFT modeling will be employed. The project would require fundamental investigations
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(CFD) and density functional theory (DFT) forms a powerful in silico approach to understanding CVD at the atomic level, while kinetic Monte Carlo methods can be used to study the development of different
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on novel high-resolution microscopy techniques to experimentally quantify transition probabilities, alongside density functional theory (DFT) calculations of defects in feldspar. This inter-disciplinary
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to analyze crystallographic data and model electronic structures in complex materials. The work involves creating code for processing diffuse scattering data, performing density functional theory (DFT
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members working on novel high-resolution microscopy techniques to experimentally quantify transition probabilities, alongside density functional theory (DFT) calculations of defects in feldspar. This inter