Next-generation quantum theory of atoms in molecules for the ground and excited state of DHCL

The Analysis of Electron Densities: From Basics to Emergent Applications

The electron density determines all properties of a system of nuclei and electrons. It is both computable and observable. Its topology allows gaining insight into the mechanisms of bonding and other phenomena in a way that is complementary to and beyond that available from the molecular orbital picture and the formal oxidation state (FOS) formalism. The ability to derive mechanistic insight from electron density is also important with methods where orbitals are not available, such as orbital-free density functional theory (OF-DFT). While density topology-based analyses such as QTAIM (quantum theory of atoms-in-molecules) have been widely used, novel, vector-based techniques recently emerged such as next-generation (NG) QTAIM. Density-dependent quantities are also actively used in machine learning (ML)-based methods, in particular, for ML DFT functional development, including machine-learnt kinetic energy functionals. We review QTAIM and its recent extensions such as NG-QTAIM and localization-delocalization matrices (LDM) and their uses in the analysis of bonding, conformations, mechanisms of redox reactions excitations, as well as ultrafast phenomena. We review recent research showing that direct density analysis can circumvent certain pitfalls of the FOS formalism, in particular in the description of anionic redox, and of the widely used (spherically) projected density of states analysis. We discuss uses of density-based quantities for the construction of DFT functionals and prospects of applications of analyses of density topology to get mechanistic insight with OF-DFT and recently developed time-dependent OF-DFT.

Next generation quantum theory of atoms in molecules for the design of emitters exhibiting thermally activated delayed fluorescence with laser irradiation

The effect of a static electric (E)-field and an unchirped and chirped laser pulse field on the cycl[3.3.3]azine molecule was investigated using next-generation quantum theory of atoms in molecules (NG-QTAIM). Despite the magnitude of the E-field of the laser pulses being an order of magnitude lower than for the static E-field, the variation of the energy gap between the lowest lying singlet (S₁ ) and triplet (T₁ ) excited states was orders of magnitude greater for the laser pulse than for the static E-field. Insights into the response of the electronic structure were captured by NG-QTAIM, where differences in the inverted singlet-triplet gap due to the laser pulses were significant larger compared to those induced by the static E-field. The response of the S₁ and T₁ excited states, as determined by NG-QTAIM, switched discontinuously between weak and strong chemical character for the static E-field. In contrast, the response to the laser pulses, determined by NG-QTAIM, is to induce a continuous range of chemical character, indicating the unique ability of the laser pulses to induce polarization effects in the form of "mixed" bond types. Our analysis demonstrates that NG-QTAIM is a useful tool for understanding the response to laser irradiation of the lowest-lying singlet S₁ and triplet T₁ excited states of emitters exhibiting thermally activated delayed fluorescence. The chirped laser pulse led to more frequent instances of the desired outcome of an inverted singlet-triplet gap than the unchirped pulse, indicating its usefulness as a tool to design more efficient organic light-emitting diode devices.

Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach

We present a machine learning-assisted excited state molecular dynamics (ML-ESMD) based on the ensemble density functional theory framework. Since we represent a diabatic Hamiltonian in terms of generalized valence bond ansatz within the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS) method, we can avoid singularities near conical intersections, which are crucial in excited state molecular dynamics simulations. We train the diabatic Hamiltonian elements and their analytical gradients with the SchNet architecture to construct machine learning models, while the phase freedom of off-diagonal elements of the Hamiltonian is cured by introducing the phase-less loss function. Our machine learning models show reasonable accuracy with mean absolute errors of ∼0.1 kcal/mol and ∼0.5 kcal/mol/Å for the diabatic Hamiltonian elements and their gradients, respectively, for penta-2,4-dieniminium cation. Moreover, by exploiting the diabatic representation, our models can predict correct conical intersection structures and their topologies. In addition, our ML-ESMD simulations give almost identical result with a direct dynamics at the same level of theory.

Intramolecular mode coupling of the isotopomers of water: a non-scalar charge density-derived perspective

Left: The BCP trajectories T(s) for H₂O for the bending (Q1) mode, the axes labels of the trajectory T(s). The green spheres correspond to the bond critical point (BCPs). Right: The corresponding T(s) for H₂O for the symmetric-stretch (Q2) mode.