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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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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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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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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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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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tuneable materials for Earth-abundant solid-state electrolytes using atomistic simulations (primarily density functional theory, DFT, and molecular dynamics, MD) as well as developing machine learning models
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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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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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theory (DFT) and related computational methods. Your work will contribute to predicting and deepening our understanding of electronic, structural, and magnetic properties at solid-state surfaces and