Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings from Random Structure Search and Experiments.

Machine learning is changing how we design and interpret experiments in materials science. In this work, we show how unsupervised learning, combined with random structure searching, improves our understanding of structural metastability in multicomponent alloys. We focus on the case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects.

Understanding the microstructural evolution and mechanical properties of transparent Al-O-N and Al-Si-O-N films.

Optically transparent, colorless Al-O-N and Al-Si-O-N coatings with discretely varied O and Si contents were fabricated by reactive direct current magnetron sputtering (R-DCMS) from elemental Al and Si targets and O and N reactive gases. The Si/Al content was adjusted through the electrical power on the Si and Al targets, while the O/N content was controlled through the O flow piped to the substrate in addition to the N flow at the targets. The structure and morphology of the coatings were studied by X-ray diffraction (XRD) and transmission electron microscopy (TEM), while the elemental composition was obtained from Rutherford backscattering spectrometry (RBS) and heavy ion elastic recoil detection analysis (ERDA).

Beam delivery and pulse compression to sub-50 fs of a modelocked thin-disk laser in a gas-filled Kagome-type HC-PCF fiber.

We present two experiments confirming that hypocycloid Kagome-type hollow-core photonic crystal fibers (HC-PCFs) are excellent candidates for beam delivery of MW peak powers and pulse compression down to the sub-50 fs regime. We demonstrate temporal pulse compression of a 1030-nm Yb:YAG thin disk laser providing 860 fs, 1.9 µJ pulses at 3.