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materials, organic solids, molecular materials, or other solid-state systems. Experience with computational approaches relevant to structural characterization, including density functional theory (DFT
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
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theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and machine learning methods. Provide theoretical
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. An ideal candidate should have experience in modeling electrochemical reactions on surfaces and interfaces using first-principles density functional theory (DFT), grand canonical DFT (GC-DFT
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of spintronics. Complemented with density functional theory (DFT) calculations to build scientific and technical competence as well as strengthen transferrable skills, this position provides you with the skills
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and post-DFT methods) for generating datasets to train AI models leveraging DOE’s HPC platforms Develop new methodologies that can describe both atomic and spin relaxation accurately but at a much