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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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. 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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, 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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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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diffractometer, a GC for common 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
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in Toulouse: the LNCMI (transport measurements at very high fields), the CEMES (theory, DFT), and the LAAS (epitaxial growth and nanofabrication). Where to apply Website https://emploi.cnrs.fr/Candidat
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' (http://www.quanthic.org ) within the Institut of Chemistry for Life and Health Sciences at Chimie ParisTech -PSL (Paris, France). This work is financed under the ANR ElectroHIP project. Where to apply