Crystal phases of soft spheres systems in a slab geometry

We have identified the ground state configurations of soft particles (interacting via inverse power potentials) confined between two hard, impenetrable walls. To this end we have used a highly reliable optimization scheme at {\it vanishing} temperature while varying the wall separation over a representative range. Apart from the expected layered triangular and square structures (which are compatible with the three dimensional bulk fcc lattice), we have identified a cascade of highly complex intermediate structures. Taking benefit of the general scaling properties of inverse power potentials, we could identify -- for a given softness value -- one single master curve which relates the energy to the wall separation, irrespective of the density of the system. Via extensive Monte Carlo simulations, we have performed closer investigations of these intermediate structures at {\it finite} temperature: we could provide evidence to which extent these particle arrangements remain stable over a relatively large temperature range

Angle-Resolved Photoemission of Solvated Electrons in Sodium-Doped Clusters

Angle-resolved photoelectron spectroscopy of the unpaired electron in sodium-doped water, methanol, ammonia, and dimethyl ether clusters is presented. The experimental observations and the complementary calculations are consistent with surface electrons for the cluster size range studied. Evidence against internally solvated electrons is provided by the photoelectron angular distribution. The trends in the ionization energies seem mainly determined by the degree of hydrogen bonding in the solvent and the solvation of the ion core. The onset ionization energies of water and methanol clusters do not level off at small cluster sizes, but decrease slightly with increasing cluster size

Self-assembly scenarios of patchy colloidal particles

The rapid progress in precisely designing the surface decoration of patchy colloidal particles offers a new, yet unexperienced freedom to create building entities for larger, more complex structures in soft matter systems. However, it is extremely difficult to predict the large variety of ordered equilibrium structures that these particles are able to undergo under the variation of external parameters, such as temperature or pressure. Here we show that, by a novel combination of two theoretical tools, it is indeed possible to predict the self-assembly scenario of patchy colloidal particles: on one hand, a reliable and efficient optimization tool based on ideas of evolutionary algorithms helps to identify the ordered equilibrium structures to be expected at T = 0; on the other hand, suitable simulation techniques allow to estimate via free energy calculations the phase diagram at finite temperature. With these powerful approaches we are able to identify the broad variety of emerging self-assembly scenarios for spherical colloids decorated by four patches and we investigate and discuss the stability of the crystal structures on modifying in a controlled way the tetrahedral arrangement of the patches.Comment: 11 pages, 7 figures, Soft Matter Communication (accepted

Spectroscopic Studies Of Aluminum Monofluoride With Relevance For Laser Cooling And Trapping

Aluminum monofluoride (AlF) is an excellent candidate for laser cooling on any Q-line of the A$^1\Pi$~-~X$^1\Sigma$ transition and trapping at high densities.\footnote{Truppe et al., Phys. Rev. A 100, 052513 (2019)}\\ In preparation for cooling and manipulation experiments, it is necessary to know the detailed energy structure of the involved states, as well as their lifetimes, dipole moments and the Franck-Condon factors of their transitions.\\ The metastable a$^3\Pi$ state is the ideal starting point for extensive spectroscopic investigations. Therefore, this presentation will focus on the a$^3\Pi \leftarrow$~X$^1\Sigma^+$ transition. The energy levels in the X$^1\Sigma^+, v''=0$ state and within each $\Omega$ manifold of the a$^3\Pi, v'=0$ state were determined with a relative accuracy of a few kHz, using laser-radio-frequency multiple resonance and ionization detection schemes in a jet-cooled, pulsed molecular beam. All spectroscopic parameters relevant for describing the rotational and hyperfine structure were determined by fitting the eigenvalues of the molecular Hamiltonian to the data.\\ With this knowledge, the measured hyperfine structure in the A$^1\Pi$ state could be assigned. The dipole moments of the X$^1\Sigma^+$, A$^1\Pi$ and a$^3\Pi$ states were determined by recording cw excitation spectra in electric fields up to 150~kV/cm.\\ The A$^1\Pi$~-~a$^3\Pi$ band was observed for the first time. Measurements on the transition strength showed that it is no significant loss channel for the A$^1\Pi$~-~X$^1\Sigma$ laser cooling transition.\

Novel Ground-State Crystals with Controlled Vacancy Concentrations: From Kagom\'{e} to Honeycomb to Stripes

We introduce a one-parameter family, $0 \leq H \leq 1$, of pair potential functions with a single relative energy minimum that stabilize a range of vacancy-riddled crystals as ground states. The "quintic potential" is a short-ranged, nonnegative pair potential with a single local minimum of height $H$ at unit distance and vanishes cubically at a distance of \rt. We have developed this potential to produce ground states with the symmetry of the triangular lattice while favoring the presence of vacancies. After an exhaustive search using various optimization and simulation methods, we believe that we have determined the ground states for all pressures, densities, and $0 \leq H \leq 1$. For specific areas below 3\rt/2, the ground states of the "quintic potential" include high-density and low-density triangular lattices, kagom\'{e} and honeycomb crystals, and stripes. We find that these ground states are mechanically stable but are difficult to self-assemble in computer simulations without defects. For specific areas above 3\rt/2, these systems have a ground-state phase diagram that corresponds to hard disks with radius \rt. For the special case of H=0, a broad range of ground states is available. Analysis of this case suggests that among many ground states, a high-density triangular lattice, low-density triangular lattice, and striped phases have the highest entropy for certain densities. The simplicity of this potential makes it an attractive candidate for experimental realization with application to the development of novel colloidal crystals or photonic materials.Comment: 25 pages, 11 figure

Facetted patchy particles through entropy-driven patterning of mixed ligand SAMS

We present a microscopic theory that describes the ordering of two distinct ligands on the surface of a faceted nanoparticle. The theory predicts that when one type of ligand is significantly bulkier than all others, the larger ligands preferentially align themselves along the edges and vertices of the nanoparticle. Monte Carlo simulations confirm these predictions. We show that the intrinsic conformational entropy of the ligands stabilizes this novel edge-aligned phase.Comment: 11 pages, 10 figure

Transfer Learning-Based Modular Neural Network for Multi-Objective Optimization of Interior Permanent Magnet Synchronous Motors

Finite element analysis is frequently used to optimize the characteristics of interior permanent magnet synchronous motors throughout the design phase. The existing toolchains enable the full automation of simulating and optimizing a reference motor by manipulating the input design parameters within the feasible design space. However, for each motor design, a complete simulation is required, implying a high computational burden and time cost. Moreover, once the input design parameters undergo variations, it becomes necessary to initiate the simulation process from the beginning. The previously obtained simulation results are not helpful for the new task. In this paper, a new method using modular neural networks based on transfer learning (TL) under dimensionally varying input space conditions is presented. By transferring certain parts of the pre-trained neural networks (NNs) of the old task to the new task\u27s NN, the previously learned parameters can be applied as the initial weight for the new network. Finally, the optimization process is completed by combining this approach with multi-objective optimization. The results show that the learning of the new NN is promoted with the help of TL. In addition, highly flexible surrogate models are achieved, enabling accurate prediction capabilities and a fast optimization time of around 12 seconds