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Field
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Description Is the vast conformational space of oligopeptides and proteins amenable to ab initio structural predictions? We will propose multidisciplinary research that combines state-of-the-art computational
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into interfacial thermal transport. The goals are to: run ab-initio molecular simulations to sample relevant nanomaterial/liquid interfaces. construct new MLPs by using generated data from 1. and validate them. use
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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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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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, ab-initio molecular dynamics), machine learning, and python coding, with the use of national supercomputing architecture. Within these simulations, the candidate will perform calculations regarding
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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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with fully ab initio theoretical approaches. Due to the project nature, a base requirement for applicants is an interest in simulations of nanosystems using quantum-mechanical methods. Ideally
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magnetometry and EPR spectroscopy, supported by ab initio calculations. For publications relevant to the project see: Nature Chemistry, 2021, 13, 243, and Eur. J. Inorg. Chem., 2022, e202101063. Experience in
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. Correlated experimental, ab initio and multi-scale techniques are central to our mission: Development and application of advanced simulation techniques to explore and identify the fundamental structures and
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expected to know theoretical methods of ab initio electronic structure calculations and/or nuclear dynamics for small molecules and/or related topics in ultracold quantum many-body physics. We also expect