-
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
-
development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
-
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
-
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