Importance of Electron Correlation on the Geometry and Electronic Structure of [2Fe-2S] Systems: A Benchmark Study of the [FeS(SCH)], [FeS(SCys)], [FeS(S--tol)], and [FeS(S--xyl)] Complexes.

Iron-sulfur clusters are crucial for biological electron transport and catalysis. Obtaining accurate geometries, energetics, manifolds of their excited electronic states, and reduction energies is important to understand their role in these processes. Using a [2Fe-2S] model complex with Fe and Fe oxidation states, which leads to different charges, i.

Metavalent or Hypervalent Bonding: Is There a Chance for Reconciliation?

A family of solids including crystalline phase change materials such as GeTe and Sb Te , topological insulators like Bi Se and halide perovskites such as CsPbI possesses an unconventional property portfolio that seems incompatible with ionic, metallic, or covalent bonding. Instead, evidence is found for a bonding mechanism characterized by half-filled p-bands and a competition between electron localization and delocalization. Different bonding concepts have recently been suggested based on quantum chemical bonding descriptors which either define the bonds in these solids as electron-deficient (metavalent) or electron-rich (hypervalent).

Role of Spin Polarization and Dynamic Correlation in Singlet-Triplet Gap Inversion of Heptazine Derivatives.

The new generation of proposed light-emitting molecules for organic light-emitting diodes (OLEDs) has raised considerable research interest due to its exceptional feature─a negative singlet-triplet (ST) gap violating Hund's multiplicity rule in the excited S and T states. We investigate the role of spin polarization in the mechanism of ST gap inversion. Spin polarization is associated with doubly excited determinants of certain types, whose presence in the wave function expansion favors the energy of the singlet state more than that of the triplet.

Revisiting the Nature of Chemical Bonding in Chalcogenides to Explain and Design their Properties.

Quantum chemical bonding descriptors have recently been utilized to design materials with tailored properties. Their usage to facilitate a quantitative description of bonding in chalcogenides as well as the transition between different bonding mechanisms is reviewed. More importantly, these descriptors can also be employed as property predictors for several important material characteristics, including optical and transport properties.

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems.

Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems.