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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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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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matter physics, with a staff of 450, including 175 researchers and lecturers. The MEM laboratory (CEA Grenoble) conducts research on the exploration of materials and devices using advanced simulation
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to investigate and develop novel ceramic electrolytes for next-generation composite solid-state batteries. Particularly halides and sulfides are of interest. By integrating atomistic simulations with experimental
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and validate its accuracy against previous benchmarks for small molecules like PtH. This approach is general and can be directly combined with EOM-CC embedded in point charges, or in periodic DFT
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and validate its accuracy against previous benchmarks for small molecules like PtH. This approach is general and can be directly combined with EOM-CC embedded in point charges, or in periodic DFT
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization