-
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
-
. 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
-
numerical calculations (DFT...) to achieve the objectives of the project. Develop adequat numerical methods necessary to reach these goals. Study of the Pt/Co/HfO2 interface: - Calculate the thermodynamical
-
, 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
-
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
-
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
-
optimization methods for catalytic systems, such as highthrouput experimentation (HTE, collaboration with the HTE platform of the CEA Saclay) and DFT computations, and be trained in these techniques if he/she so
-
, 7503–7507 (2015) • collaborate with theoreticians at CEMES to compare experiments with the band structure first-principles calculations based on the density functional theory (DFT). The National Intense