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of materials under pressure, with a view to identifying and formalizing rules and concepts; - Applying the DFT/MLIP “Crystal Structure Prediction CSP” methodology to various issues addressed by the research
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-41 https://doi.org/10.1016/j.psep.2020.07.017 🎓 What you will gain • Strong expertise in advanced kinetic modeling • Hands-on experience with calorimetric instrumentation • Exposure to DFT and AI
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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motivated to acquire new skills. Candidates must be fluent in English and/or French with scientific writing skills. The doctoral contract will take place at the CRISMAT laboratory (https://crismat.cnrs.fr
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, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust
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, combining theoretical (DFT modeling) and experimental approaches. - Study of reaction mechanisms: Identify key intermediates and reaction pathways that promote the formation of C–C bonds, using in situ
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of oxygenates from CO and/or CO2, and mechanistic studies including operando spectroscopy and possibly numerical simulations (microkinetics, DFT). The objective is to discover new eco-efficient catalytic phases
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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of chemically storing and releasing hydrogen, using methanol as a reservoir. Main activities: • Utilize global optimization codes and perform DFT calculations on supercomputers. • Analyze results and
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gas analysis, a GC/MS, and an HPLC/MS. DFT calculations will be performed using annual allocations on national high-performance computing centers. More details here: https://iramis.cea.fr/en/nimbe/lcmce