Sort by
Refine Your Search
-
will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
-
, and battery materials. LPENS is a joint research unit of ENS-PSL, CNRS, Sorbonne Université, and Université Paris Cité, covering a broad spectrum of fundamental, theoretical, and experimental physics
-
of spintronics. Complemented with density functional theory (DFT) calculations to build scientific and technical competence as well as strengthen transferrable skills, this position provides you with the skills
-
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
-
lifetimes of spins on surfaces. This approach combines electronic states obtained via a periodic quantum embedding (i.e., equation-of-motion coupled-cluster in periodic DFT, pbcEOM-CC) with a coarse-grained
-
simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization