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be delocalised, hence describable with standard DFT exchange and correlation, or localised and subject to strong correlations. This aspect and the local structural and compositional environment
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(CFD), including chemical reaction models, but could also involve other modeling methods such as Density Functional Theory (DFT) or kinetic Monte Carlo. Your qualifications You have graduated at Master’s
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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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the use of excited-state methods (TD-DFT, ADC(2), etc.) to explore the relationships between the molecular structures, photoinduced electron transfer (PeT), and chirality in strong connexion with
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Qualifications Familiar with Ab-initio calculation packages (VASP(plane wave basis set), JAGUAR(Gaussian basis sets), CRYSTAL(hybrid DFT with Gaussian basis sets)), MD simulation software (LAMMPS), bond-detecting
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Michelle Spencer (michelle.spencer@rmit.edu.au ), specifically mentioning this scholarship, to express their interest and apply through the RMIT application process. Candidates with prior experience in DFT
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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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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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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