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the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials discovery. The position
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this position, you will employ molecular dynamics (MD) simulations to investigate the underlying atomistic mechanisms of LCI in steel grain boundaries and the inhibitory role of silicon. Your MD-based approach
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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spin-lattice dynamics simulations, a framework that combines atomistic spin dynamics (magnons) with molecular dynamics (phonons), to investigate ways of manipulating spins in various magnetic systems
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adaptive integration methods designed to accelerate atomistic simulations. The approach to be developed will initially integrate spectroscopy data (XPS, SAX, SXRD) to generate candidate structures for S-S
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atomistic resolution. However, many chemically relevant processes involve rare events and activated transitions that occur on timescales inaccessible to conventional molecular dynamics simulations. Overcoming
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work
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the preponderance of surface and finite size effects. This project aims to dynamically track temperature-induced phase transitions at the nanoscale through atomistic simulations. Focusing on metallic alloys and
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design rules to understand their chemistry and physics. You will combine coarse-grained and atomistic simulations with surrogate models and experimental insights (with Dr. Baumgartner) to understand
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and multiscale modelling of helium effects in irradiated Fe–Cr alloys. The position focuses on computational materials science and atomistic simulations. The successful candidate will contribute