Compositional optimization of hard-magnetic phases with machine-learning models

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 hard-magnetic 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 Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

High-Throughput Screening of Rare-Earth-Lean Intermetallic 1-13-X Compounds for Good Hard-Magnetic Properties

By computational high-throughput screening, the spontaneous magnetization Ms, uniaxial magnetocrystalline anisotropy constant K₁, anisotropy field Ha, and maximum energy product (BH)max are estimated for ferromagnetic intermetallic phases with a tetragonal 1-13-X structure related to the LaCo₉Si₄ structure type. For SmFe₁₃N, a (BH)max as high as that of Nd₂Fe₁₄B and a comparable K₁ are predicted. Further promising candidates of composition SmFe₁₂AN with A = Co, Ni, Cu, Zn, Ga, Ti, V, Al, Si, or P are identified which potentially reach (BH)max values higher than 400 kJ/m³ combined with significant K₁ values, while containing almost 50% less rare-earth atoms than Nd₂Fe₁₄B

Impact of Hydrogen Concentration on the Regeneration of Light Induced Degradation

AbstractThe permanent deactivation called regeneration of light induced degradation in p-type Czochralski silicon solar cells is analyzed in this paper. Industrial solar cells were fabricated with varying hydrogen concentration in the silicon nitride anti-reflection layer but with an otherwise identical setup. They are subsequently degraded, annealed and regenerated by simultaneous illumination and heating. Measurements of cell parameters reveal the crucial effect of hydrogen on the regeneration