We report on theoretical investigations of intermetallic phases derived from the ThMn12-type crystal structure. Our computational high-throughput screening (HTS) approach is extended to an estimation of the anisotropy constant K1, the anisotropy field Ha and the energy product (BH)max. The calculation of K1 is fast since it is based on the crystal field parameters and avoids expensive total-energy calculations with many k-points. Thus the HTS approach allows a very efficient search for hard-magnetic materials for which the magnetization M and the coercive field Hc connected to Ha represent the key quantities. Besides for NdFe12N which has the highest magnetization we report HTS results for several intermetallic phases based on Cerium which are interesting as alternative hard-magnetic phases because Cerium is a less ressource-critical element than Neodymium.

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Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hardmagnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd2Fe14B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements. arXiv:1803.03073v2 [cond-mat.mtrl-sci]

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Stacks of aluminum oxide and silicon nitride are frequently used in silicon photovoltaics. In this Letter, we demonstrate that hydrogenated aluminum nitride can be an alternative to this dual-layer stack. Deposited on 1 ohm cm p-type FZ silicon, very low effective surface recombination velocities of 8 cm/s could be reached after firing at 820 °C. This excellent passivation is traced back to a high density of fixed charges at the interface of approximately -1 × 1012 cm-2 and a very low interface defect density below 5 × 1010 eV-1 cm-2. Furthermore, spectral ellipsometry measurements reveal that these aluminum nitride layers have ideal optical properties for use as anti-reflective coatings