The Extended Koopman’s Theorem (EKT) [1,2] provides a straightforward way to compute charged excitations from any level of theory.
In this work we get insight into the quality of removal/addition energies obtained using the EKT within reduced density matrix functional theory (RDMFT) [3]. 

The coupling between spin and valley degrees of freedom is one of the most intriguing properties of transition metal dichalcogenides (TMDs). This effect allows us to populate a single spin and valley combination using a circularly polarised laser. However, it has been shown that intrinsic properties alone cannot sustain long lived spin signals and that these must come from extrinsic properties [1].

The presence of a plasmonic nanoparticle strongly affects the optical response of molecules, leading to significant effects such as surface enhanced Raman scattering, surface enhanced infrared absorption and metal enhanced fluorescence. Moreover, in recent years plasmonic metallic nanostructures have emerged as a new family of photocatalysts, enhancing rates and increasing selectivity of important chemical reactions such as H2 splitting and CO2 reduction to methane.

The investigation of the coupling of optically excited, bound electron-hole pairs – i.e., excitons – with phonons has become increasingly relevant to both experimental and theoretical research, being related to many spectroscopic features that are nowadays observable in low-dimensional semiconductors.

Solving models in quantum physics is of paramount importance to understanding a variety of physical phenomena.  There are many methods that have been created to do this. Tensor networks are one strong candidate that have been extremely popular in solving models with local interactions [1,2].  These methods are motivated from a combination of information theory and renormalization group techniques.  They also naturally compute the entanglement of a given model, making them useful for studies of topological physics.

Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. [1–3] These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming.

The accurate description of spin-state energetics of transition-metal complexes represents a great challenge for electronic structure ab initio methods.This challenge stems from the lack of error cancellation when computing energy