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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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will be put on the effect of structural defects on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will
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pathway. Additionally, finite element theoretical modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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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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sensing, to improve worker/operator safety. This project will focus on using density functional theory calculations and ab initio molecular dynamics simulations. The project is a collaboration with Dr
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Carlo (MC), as well as quantum approaches like Density Functional Theory (DFT). Develop novel Graph Neural Network (GNN) potentials to accurately represent the catalytic behavior of specific species
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density within these films, together with a non-uniform distribution of counterions and specific ion effects but there is no direct evidence supporting this theory. The task of this job is to investigate
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liquid environment. 1) Molecular Modeling - Study of interactions between MnO₂ and ionic liquids using Density Functional Theory (DFT) and Reactive Molecular Dynamics (ReaxFF). - Analysis of oxidation