Vibrationally resolved near-edge x-ray absorption spectra at the K-edge for a number of small molecules have been computed from anharmonic vibrational configuration interaction calculations of the Franck-Condon factors. The potential energy surfaces for ground and core-excited states were obtained at the core-valence separated CC2, CCSD, CCSDR(3), and CC3 levels of theory, employing the adaptive density-guided approach scheme to select the single points at which to perform the energy calculations. We put forward an initial attempt to include pair-mode coupling terms to describe the potential of polyatomic molecules.Thermally driven processes of molecular systems include transitions of energy barriers on the microsecond timescales and higher. Sufficient sampling of such processes with molecular dynamics simulations is challenging and often requires accelerating slow transitions using external biasing potentials. Different dynamic reweighting algorithms have been proposed in the past few years to recover the unbiased kinetics from biased systems. However, it remains an open question if and how these dynamic reweighting approaches are connected. In this work, we establish the link between the two main reweighting types, i.e., path-based and energy-based reweighting. We derive a path-based correction factor for the energy-based dynamic histogram analysis method, thus connecting the previously separate reweighting types. We show that the correction factor can be used to combine the advantages of path-based and energy-based reweighting algorithms it is integrator independent, more robust, and at the same time able to reweight time-dependent biases. We can furthermore demonstrate the relationship between two independently derived path-based reweighting algorithms. Our theoretical findings are verified on a one-dimensional four-well system. By connecting different dynamic reweighting algorithms, this work helps to clarify the strengths and limitations of the different methods and enables a more robust usage of the combined types.Optical cavities, e.g., as used in organic polariton experiments, often employ low finesse mirrors or plasmonic structures. The photon lifetime in these setups is comparable to the timescale of the nuclear dynamics governing the photochemistry. This highlights the need for including the effect of dissipation in the molecular simulations. In this study, we perform wave packet dynamics with the Lindblad master equation to study the effect of a finite photon lifetime on the dissociation of the MgH+ molecule model system. Photon lifetimes of several different orders of magnitude are considered to encompass an ample range of effects inherent to lossy cavities.Metal-ligand cluster ions are structurally characterized by means of gas-phase infrared multiple photon dissociation spectroscopy. The mass-selected complexes consist of one or two metal cations M3+ (M = Al, Fe, or Ru) and two to five anionic bidentate acetylacetonate ligands.  https://www.selleckchem.com/products/tiragolumab-anti-tigit.html  Experimental IR spectra are compared with different density functional theory calculations, namely, PBE/TZVP, B3LYP/6-31G*, and M06/6-31+G**. Frequency analysis was also performed at different levels, namely, scaled static harmonic and unscaled static anharmonic, or with ab initio molecular dynamics simulations at the PBE/TZVP level. All methods lead to simulated spectra that fit rather well with experimental data, and the spectral red shifts of several main bands, in the 1200 cm-1-1800 cm-1 range, are sensitive to the strength of the metal-ligand interaction and to the spin state of the ion. Due to the rigidity of those complexes, first principles molecular dynamics calculations provide spectra similar to that produced by static calculations that are already able to catch the main spectral signatures using harmonic calculations at the B3LYP/6-31G* level.Local optimization of adsorption systems inherently involves different scales within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.The structure of nanoconfined fluids is particularly non-uniform owing to the wall interaction, resulting in the distinctive characteristic of thermal transport compared to bulk fluids. We present the molecular simulations on the thermal transport of water confined in nanochannels with a major investigation of its spatial distribution under the effects of wall interaction. The results show that the thermal conductivity of nanoconfined water is inhomogeneous and its layered distribution is very similar to the density profile. The layered thermal conductivity is the coupling result of inhomogeneous density and energy distributions that are generally diametrical, and their contributions to the thermal conductivity compensate with each other. However, the accumulative effect of water molecules is really dominating, resulting in a high thermal conductivity in the high-density layers with the low-energy molecules, and vice versa. Moreover, it is found that the adsorptive and repulsive interactions from solid walls have different roles in the hierarchical thermal transport in nanoconfined water. The adsorptive interaction is only responsible for the layered distribution of thermal conductivity, while the repulsive interaction is responsible for the overall thermal conductivity; accordingly, the thermal conductivity is independent of the strength of water-solid interactions. The identified hierarchical thermal transport in nanoconfined water and its underlying mechanisms have a great significance for the understanding of nanoscale thermal transport and even the mass and energy transport of nanoconfined fluids.