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Starting from nanocrystalline and submicron wurtzite-aluminum nitride (w-AlN) powder rocksalt structure (rs-AlN) samples were synthesized by two different methods of shock wave recovery experiments. The resulting samples contained up to 86% rs-AlN, stable at room temperature, giving for the first time the possibility to comprehensively characterize the material by powder X-ray diffraction, Fourier transform infrared (IR), Raman, and 27 Al NMR spectroscopy. Raman and IR modes were calculated by density functional theory, allowing for the interpretation of the respective experimental spectra. By 27 Al NMR the chemical shift of rsAlN was determined, and the quadrupolar coupling constant was estimated.

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The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.

Contact less measurements of the minority carrier “lifetime” and the photoconductivity are widely used to characterize the material quality and to investigate defects in a sample. In order to interpret these measurements correctly and to guarantee comparability between different methods, numerical simulation tools were developed. These simulations allow to account even for very complex defect models, thus, e.g., enabling the simulation of trapping effects. Contrary to the Shockley–Read–Hall model or the widely used simulation tool PC1D nearly no assumptions are made. Furthermore, nonsteady state solutions can be obtained. The simulation approach is explained in detail, along with simulations of the trapping effect on the measured lifetime for different injections, trap parameters, and measuring methods, demonstrating the capabilities of the here presented simulation tool. Temperature and injection dependent lifetime measurements were performed and it is shown how important sample parameters can be extracted using the simulation tool. Additionally an approach is presented to simulate lifetimes for thick samples, where a nonuniform carrier profile has to be taken into account. This enables a comparison of nonsteady state to steady-state lifetime measurement techniques even for thick samples such as ingots.