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on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will also exploit machine-learning techniques to train more
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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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experimental investigation for battery development, both supported by the Icelandic Research Fund (RANNIS), a project grant for 3 years. Main tasks The first project involves density functional theory (DFT
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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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(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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advanced structural analyses methods and DFT calculations the H-bonding networks involving water molecules and SbW6 units, responsible of PL quenching effects. - to optimize the recycling procedures
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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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reference calculation by DFT, large-scale generation of training data, training and modifying published NN models on custom datasets, and evaluation of their performance. The ultimate goal will be to remove