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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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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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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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effects in magnets, this project will develop Density Functional Theory methodology for multicomponent lanthanide materials. We will study, for example, how application of pressure causes the f-electrons in
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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