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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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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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chemistry software (e.g., Gaussian, Dalton, TurboMole) is highly desirable. Background in Density Functional Theory (DFT) and Time-Dependent Density Functional Theory (TD-DFT) are desirable assets. Strong
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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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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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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
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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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energies will be measured through threshold collision-induced dissociation (T-CID) experiments, with tandem MS/MS experiments used to ensure accuracy. Computational methods like Density Functional Theory
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energies will be measured through threshold collision-induced dissociation (T-CID) experiments, with tandem MS/MS experiments used to ensure accuracy. Computational methods like Density Functional Theory