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One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the 23 Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the 23 Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Si n -Al sequence length. Furthermore, neglect of thermal and dynamical contributions was found to lead to errors of several ppm, and has a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for the 23 Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect of dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.

Solid state (ss-) 27 Al NMR is one of the most valuable tools for the experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the interpretation of ss-NMR is complex and the determination of aluminum distributions remains generally unfeasible. As a result, computational modelling of 27 Al ss-NMR spectra has grown increasingly popular as a means to support experimental characterization. However, a number of simplifying assumptions are commonly made in NMR modelling, several of which are not fully justified. In this work, we systematically evaluate the effects of various common models on the prediction of 27 Al NMR chemical shifts in zeolites CHA and MOR. We demonstrate the necessity of operando modelling; in particular, taking into account the effects of water loading, temperature and the character of the charge-compensating cation. We observe that conclusions drawn from simple, high symmetry model systems such as CHA do not transfer well to more complex zeolites and can lead to qualitatively wrong interpretations of peak positions, Al assignment and even the number of signals. We use machine learning regression to develop a simple yet robust relationship between chemical shift and local structural parameters in Al-zeolites. This work highlights the need for sophisticated models and high-quality sampling in the field of NMR modelling and provides correlations which allow for the accurate prediction of chemical shifts from dynamical simulations., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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 Gd 3+ 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 Gd 3+ considerably increases with growing ionic radius and concentration of network-modifier ions. In addition, structure predictions indicate a low driving force for clustering of Gd 3+ , 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.

Solid solutions of SiO 2 and B 2 O 3 in Li 2 O·2SiO 2 are synthesized and characterized for the first time. Their structure and crystallization mechanisms are investigated employing a combination of simulations at the density functional theory level and experiments on the crystallization of SiO 2 and B 2 O 3 doped lithium disilicate glasses. The remarkable agreement of calculated and experimentally determined cell parameters reveals the preferential, kinetically controlled incorporation of [SiO 4 ] and [BO 4 ] at the Li + lattice sites of the Li 2 O·2SiO 2 crystal structure. While the addition of SiO 2 increases the glass viscosity resulting in lower crystal growth velocities, glasses containing B 2 O 3 show a reduction of both viscosities and crystal growth velocities. These observations could be rationalized by a change of the chemical composition of the glass matrix surrounding the precipitated crystal phase during the course of crystallization, which leads to a deceleration of the attachment of building units required for further crystal growth at the liquid-crystal interface.

The activation of yellow arsenic is possible with the silylene [PhC(NtBu) 2 SiN(SiMe 3 ) 2 ] (1) and the disilene [(Me 3 Si) 2 N(η 1 -Me 5 C 5 )Si=Si(η 1 -Me 5 C 5 )N(SiMe 3 ) 2 ] (3). The reaction of As 4 with 1 leads to the unprecedented As 10 cage compound [(LSiN(SiMe 3 ) 2 ) 3 As 10 ] (2; L=PhC(NtBu) 2 ) with an As 7 nortricyclane core stabilized by arsasilene moieties containing silicon(II)bis(trimethylsilyl)amide substituents. In contrast, the compound [Cp*{(SiMe 3 ) 2 N}SiAs] 2 (4) containing a butterfly-like diarsadisilabicyclo[1.1.0]butane unit is formed by the reaction of As 4 with the disilene 3. Both compounds were characterized by single-crystal X-ray diffraction analysis, NMR spectroscopy, and mass spectrometry. The reaction outcomes demonstrate the different reaction behavior of yellow arsenic (As 4 ) compared to white phosphorus (P 4 ) in the reactions with the corresponding silylenes and disilenes., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)