Interactions between molecular hydrogen and ions are of interest in cluster science, astrochemistry and hydrogen storage. In dynamical simulations, H2 molecules are usually modelled as point particles, an approximation that can fail for anisotropic interactions. Here, we apply an adiabatic separation of the H2 rotational motion to build effective pseudoatom-ion potentials and in turn study the properties of (H2)nNa+/Cl− clusters. These interaction potentials are based on high-level ab initio calculations and Improved Lennard-Jones parametrizations, while the subsequent dynamics has been performed by quantum Monte Carlo calculations. By comparisons with simulations explicitly describing the molecular rotations, it is concluded that the present adiabatic model is very adequate. Interestingly, we find differences in the cluster stabilities and coordination shells depending on the spin isomer considered (para- or ortho-H2), especially for the anionic clusters.

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Silicon nanostructures have a rich optical response thanks to Mie-type optical resonances, that can be designed on-demand via their geometry. It is possible to encode bits of information in a nanostructure’s geometry, and retrieve this information optically via the color observed in dark-field microscopy. Furthermore, asymmetric structures can profit from the illuminating light polarization to facilitate information readout. Our ultimate goal is to accurately reverse engineer experimentally feasible silicon nanostructures for information encoding, such that they implement a set of ideally distinguishable colors for robust optical readout. Deep learning is increasingly being used to solve inverse problems such as nano-photonic structure design. Neural networks for inverse design are mostly trained on simulated data, which is cheap to generate. But training neural networks on experimental data is a very interesting option, because it allows to include all experimental constraints into the model, which consequently learns to capture phenomena that may be hard to simulate. Here, in order to learn an accurate model for the full experimental measurement setup, we trained a neural network with experimental darkfield color data from several thousand nanostructures. Firstly, we built a forward network, taking as input the nanostructures’ shapes from fabricated samples and predicting the dark-field color for both X and Y polarizations. We then successfully built an inverse tandem network, capable of designing structures with desired color responses. In order to create distinguishable color responses, another deep neural network was trained on the task to map all experimental colors in a regularized color latent space. Sampling equidistant points from this latent space then yields the most distinguishable, yet experimentally feasible colors. The next future step will be to produce samples from the generated structures to test the network’s accuracy. We would like to test how many bits of information we can encode using the darkfield color as readout.

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Several dynamical processes involving Helium-4 nanodroplets (HNDs) are studied theoretically, in relation with experiments. HNDs are clusters of several hundred to several hundred billions of 4He atoms which exhibit remarkable properties: very low temperature, ~0.4K, superfluid properties, ability to pickup any dopant, weak interaction with any atom or molecule. The studied processes reflect the two main interests in HNDs: characterizing superfluid properties in a finite-size system (quantum vortex nucleation and detection), and using HNDs as an ideal environment to study dopant spectroscopy and dynamics (clustering, ion solvation, and Coulomb explosion). Extensive simulations are conducted using 4He-Density Functional Theory (4He-DFT) and its time-dependent version (4He-TDDFT). This approach can successfully simulate the equilibrium and dynamics of droplets of several thousand of atoms and provide detailed insight into the structural dynamics of the entire system which is not accessible experimentally: visualization of solvation shells, nature of helium droplet excitations. Rare gas (Rg) cluster formation is studied inside HeN under realistic conditions where one Rg atom collides with a solvated n-atom cluster to form the (n+1)-atom cluster. The 4He-DFT simulation results are compared to those of approximate atomistic approaches. Although quantum and superfluidity effects are better described with 4He-TDDFT, several common features are demonstrated. The most stable gas phase configuration is usually not produced, but an isomer with fewer bonds instead, and/or more dilute structures because of the rigidity of the helium solvation shell around the Rg atoms. The sinking of alkali (Ak) cations in HNDs is simulated in parallel with experimental investigations in the group of Stapelfeldt (Aarhus), in complement to earlier studies on Na+ sinking. It aims at shedding some light on the primary steps of solvation, by suddenly ionizing the alkali atom sitting in a dimple at the droplet surface. The build up of the first solvation shell around the ions is shown to be progressive, pointing to a Poissonian mechanism in which each He atom binds independently to the ion. For the lighter alkalis, the solvation shell is incomplete at the end of the dynamics, suggesting a kinetic rather than thermodynamical control of its formation. Coulomb explosion simulations of Ak2 molecules initially sitting at the droplet surface and suddenly ionized are conducted in order to understand the effect of the HNDs on Ak2++ fragmentation dynamics. The corresponding experiment in Stapelfeldt's group in Aarhus aimed at measuring the proportion of triplet to singlet state in the formation of Ak2, and at imaging the vibrational wave function. Several parameters are examined in the simulations: droplet size, zero point motion of Ak2 vibration, and orientational distribution of Ak2 on the droplet surface. The results validate the experimental approach, and evidence an unexpected curvature of the ion trajectories which could be used to measure droplet sizes individually, something that has only been possible up to now for very large sizes (by X-ray diffraction). The nucleation of quantum vortices, a characteristic of helium superfluidity, has been revealed in very large droplets (VLD) and attributed to angular momentum created by friction of the liquid in the nozzle prior to expansion and cooling. Here droplet-droplet collisions are explored as an alternative mechanism. The results show the nucleation of quantum vortices at indentations of the merged droplet, a mechanism general for all droplet sizes. However, no signature has been found to detect vortices in smaller droplets so far. In this work, fluorescence absorption or excitation spectroscopy of alkali atoms is proposed: a vortex is shown to shift and broaden the alkali spectrum. The effect could be measurable above the first excited states.

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The lifetimes of NeIBr were obtained for the vibrational levels and for the van der Waals (vdW) level . Was used the Quasi-Classical Trajectories (QCT) and the Trajectory Surface Hopping (TSH) under a diabatic representation. A kinetic mechanism was implemented to study the vibrational predissociation. For this purpose, a bunch of trajectories were taken in order to properly describe the energy transfer up to 5 quanta from the IBr diatom to vdW modes. The results obtained were generally consistent with previous experimental results for this system.

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