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to identify and model the most efficient catalytic sites on NDs using advanced Density Functional Theory (DFT) calculations. The project seeks to revolutionize the design of ND-based catalysts by controlling
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density functional to ab initio methods coupled to effective field theory) and related numerical and formal techniques. We are particularly fond of interdisciplinary connections and emerging technologies
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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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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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performing atomistic simulations with Density Functional Theory and Molecular Dynamics. Data analysis and coarse graining in order to provide parametrisations for upper scale models (Kinetic Monte Carlo and
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of an experimental database. In this context, one of the aims of the ALEAS project is to use two already existing experimental setups at SPEC and to instrument them accordingly: a small 10 cm radius Von Karam will