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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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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing research in mechanics of materials. Experience in theoretical solid mechanics and metallurgy
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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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functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model the molten salt
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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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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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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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(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