Since the discovery of the first "inorganic benzene" (borazine, B3N3H6), the synthesis of other noncarbon derivatives is an ongoing challenge in Inorganic Chemistry. Here we report on the synthesis of the first pnictogen-silicon congeners of benzene, the triarsa- and the triphospha-trisilabenzene [(PhC(NtBu)2)3Si3E3] (E = P (1a), As (1b)) by a simple metathesis reaction. These compounds are formed by the reaction of [Cp″2Zr(η(1:1)-E4)] (E = P, As; Cp″ = C5H3tBu2) with [PhC(NtBu)2SiCl] in toluene at room temperature along with the silicon pnictogen congeners of the cyclobutadiene, [(PhC(NtBu)2)2Si2E2] (E = P (2a), As (2b)), which is unprecedented for the arsenic system 2b. All compounds were comprehensively characterized, and density functional theory calculations were performed to verify the stability and the aromatic character of the triarsa- and the triphospha-trisilabenzene.

The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computational feasible protocol for identification of hypothetical siliceous frameworks that could be targeted experimentally is a great challenge. To tackle this challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain DFT accuracy for thermodynamic stabilities, vibrational properties, as well as reactive and non-reactive phase transformations. As a showcase, we screened an existing zeolite database and revealed >20k additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of siliceous zeolites.

Global minimum structures of neutral (Fe2O3)n clusters with n = 1-5 were determined employing the genetic algorithm in combination with ab initio parameterized interatomic potentials and subsequent refinement at the density functional theory level. Systematic investigations of magnetic configurations of the clusters using a broken symmetry approach reveal antiferromagnetic and ferrimagnetic ground states. Whereas (Fe2O3)n clusters with n = 2-5 contain exclusively Fe(3+), Fe2O3 was found to be a special case formally containing both Fe(2+) and Fe(3+). Calculated magnetic coupling constants revealed predominantly strong antiferromagnetic interactions, which exceed bulk values found in hematite. The precise magnetization (spin) state of the clusters has only small influence on their geometric structure. Starting from n = 4 also the relative energies of different cluster isomers are only weakly influenced by their magnetic configuration. These findings are important for simulations of larger (Fe2O3)n clusters and nanoparticles.

The atomic structure and properties of nanoparticulate Fe2O3 are characterized starting from its smallest Fe2O3 building unit through (Fe2O3)n clusters to nanometer-sized Fe2O3 particles. This is achieved by combining global structure optimizations at the density functional theory level, molecular dynamics simulations by employing tailored, ab initio parameterized interatomic potential functions and experiments. With the exception of nearly tetrahedral, adamantane-like (Fe2O3)2 small (Fe2O3)n clusters assume compact, virtually amorphous structures with little or no symmetry. For n = 2-5 (Fe2O3)n clusters consist mainly of two- and three-membered Fe-O rings. Starting from n = 5 they increasingly assume tetrahedral shape with the adamantane-like (Fe2O3)2 unit as the main building block. However, the small energy differences between different isomers of the same cluster-size make precise structural assignment for larger (Fe2O3)n clusters difficult. The tetrahedral morphology persists for Fe2O3 nanoparticles with up to 3 nm in diameter. Simulated crystallization of larger nanoparticles with diameters of about 5 nm demonstrates pronounced melting point depression and leads to formation of ε-Fe2O3 single crystals with hexagonal morphology. This finding is in excellent agreement with the results obtained for Fe2O3 nanopowders generated by laser vaporization and provides the first direct indication that ε-Fe2O3 may be thermodynamically the most stable phase in this size regime.

Achieving optimal solubility of active substances in polymeric carriers is of fundamental importance for a number of industrial applications, including targeted drug delivery within the growing field of nanomedicine. However, its experimental optimization using a trial-and-error approach is cumbersome and time-consuming. Here, an approach based on molecular dynamics (MD) simulations and the Flory-Huggins theory is proposed for rapid prediction of thermodynamic compatibility between active species and copolymers comprising hydrophilic and hydrophobic segments. In contrast to similar methods, our approach offers high computational efficiency by employing MD simulations that avoid explicit consideration of the actual copolymer chains. The accuracy of the method is demonstrated for compatibility predictions between pyrene and nile red as model dyes as well as indomethacin as model drug and copolymers containing blocks of poly(ethylene glycol) and poly(lactic acid) in different ratios. The results of the simulations are directly verified by comparison with the observed encapsulation efficiency of nanoparticles prepared by nanoprecipitation. © 2016 Wiley Periodicals, Inc.

The medium-range atomic structure of magnesium and barium aluminosilicate glasses doped with Gd2O3 as a model rare earth oxide is elucidated using molecular dynamics simulations. Our structure models rationalize the strong dependence of the luminescence properties of the glasses on their chemical composition. The simulation procedure used samples’ atomic configurations, the so-called inherent structures, characterizing configurations of the liquid state slightly above the glass transition temperature. This yields medium-range atomic structures of network former and modifier ions in good agreement with structure predictions using standard simulated annealing procedures. However, the generation of a large set of inherent structures allows a statistical sampling of the medium-range order of Gd3+ ions with less computational effort compared to the simulated annealing approach. It is found that the number of Si-bound non-bridging oxygen in the vicinity of Gd3+ considerably increases with growing ionic radius and concentration of network-modifier ions. In addition, structure predictions indicate a low driving force for clustering of Gd3+, yet no precise correlation between the atomic structure and luminescence lifetimes can be conclusively established. However, the structure models provided in this study can serve as a starting point for future quantum mechanical simulations to shed a light on the relation between the atomic structure and optical properties of rare earth doped aluminosilicate glasses.

Melting presents one of the most prominent phenomena in condensed matter science. Its microscopic understanding, however, is still fragmented, ranging from simplistic theory to the observation of melting point depressions. Here, a multimethod experimental approach is combined with computational simulation to study the microscopic mechanism of melting between these two extremes. Crystalline structures are exploited in which melting occurs into a metastable liquid close to its glass transition temperature. The associated sluggish dynamics concur with real‐time observation of homogeneous melting. In‐depth information on the structural signature is obtained from various independent spectroscopic and scattering methods, revealing a step‐wise nature of the transition before reaching the liquid state. A kinetic model is derived in which the first reaction step is promoted by local instability events, and the second is driven by diffusive mobility. Computational simulation provides further confirmation for the sequential reaction steps and for the details of the associated structural dynamics. The successful quantitative modeling of the low‐temperature decelerated melting of zeolite crystals, reconciling homogeneous with heterogeneous processes, should serve as a platform for understanding the inherent instability of other zeolitic structures, as well as the prolific and more complex nanoporous metal–organic frameworks.

Solid state (ss-) 27Al NMR is one of the most valuable tools for experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be...