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In the previous work, it is reported that the Spin-Seebeck effect (SSE), which refer to the generation of a spin current from a temperature gradient, can be enhanced by Fe interface treatment. Here, we investigated the Fe thickness (dFe) dependency of spin-Seebeck voltage (VSSE) and mixing conductance (gr) in Pt/Fe/Bi:YIG/SGGG system. As a result, magnitude of VSSE had a peak at dFe ⇌ 1 ML (monolayer , ⇌ 0.3 mm), and also increase of gr was saturated at this point. It suggests that VSSE increase with increasing gr when dFe is smaller than 1.0 ML. For the case in which dFe is larger than 1.0ML, however, VSSE decreases due to a spin current decay in Fe layer with a constant gr. These experimental results are consistent with previous theoretical works.

We demonstrate a heuristic optimization algorithm based on the game tree search for multi-component materials design. The algorithm searches for the largest spin polarization of seven-component Heusler alloys. The algorithm can find the peaks quickly and is more robust against local optima than Bayesian optimization approaches using the expected improvement or upper confidence bound approaches. We also investigate Heusler alloys including anti-site disorder and show that [Fe$_{0.9}$Co$_{0.1}$]$_{2}$Cr$_{0.95}$Mn$_{0.05}$Si$_{0.3}$Ge$_{0.7}$ has the potential to be a high spin polarized material with robustness against anti-site disorder., Comment: 5 pages, 6 figures

Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation, fabrication, and characterization. In particular, machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition–phase maps from structural data characterization of composition spread libraries, enabling rapid materials fabrication-structure-property analysis and functional materials discovery. A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure, or kernel function. The desired measure reduces the impact of confounding structural data issues on analysis performance. The issues include peak height changes and peak shifting due to lattice constant change as a function of composition. In this work, we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination. Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe–Co–Ni ternary alloy composition spread. The cosine, Pearson correlation coefficient, and Jensen–Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting (due to lattice constant change) when the magnitude of peak shifting is unknown. With prior knowledge of the maximum peak shifting, dynamic time warping in a normalized constrained mode provides the best performance. This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries. Using algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to overwhelming combinations of possible materials, each with different properties. A. Gilad Kusne from the National Institute of Standards and co-workers examined how machine learning techniques could simplify alloy discovery through ‘dissimilarity measures’ that quantify how key structural data points, such as the positions and intensities of X-ray peaks, change with sample makeup. The team fabricated a compositional spread of iron–cobalt–nickel thin film alloys, and then evaluated different software approaches to finding X-ray dissimilarities for both processing speed and accuracy. Several algorithms suitable for high-throughput generation of color-coded maps that display relations between alloy composition and phase in both two and three-dimensions were identified.

Heat-flow sensing is expected to be an important technological component of smart thermal management in the future. Conventionally, the thermoelectric (TE) conversion technique, which is based on the Seebeck effect, has been used to measure a heat flow by converting the flow into electric voltage. However, for ubiquitous heat-flow visualization, thin and flexible sensors with extremely low thermal resistance are highly desired. Recently, another type of TE effect, the longitudinal spin Seebeck effect (LSSE), has aroused great interest because the LSSE potentially offers favourable features for TE applications such as simple thin-film device structures. Here we demonstrate an LSSE-based flexible TE sheet that is especially suitable for a heat-flow sensing application. This TE sheet contained a Ni0.2Zn0.3Fe2.5O4 film which was formed on a flexible plastic sheet using a spray-coating method known as “ferrite plating”. The experimental results suggest that the ferrite-plated film, which has a columnar crystal structure aligned perpendicular to the film plane, functions as a unique one-dimensional spin-current conductor suitable for bendable LSSE-based sensors. This newly developed thin TE sheet may be attached to differently shaped heat sources without obstructing an innate heat flux, paving the way to versatile heat-flow measurements and management.

Thermoelectric converters based on the spin Seebeck effect (SSE) have attracted great attention due to their potential to offer novel applications such as energy harvesting and heat-flow sensing. For converting a SSE-induced spin current into an electric current, a transition metal film such as Pt, which exhibits large inverse spin-Hall effect (ISHE), has been typically used. In this work, we show an all-oxide SSE device using ruthenium oxide (RuO2) as a conductive film. We found that both the sign and magnitude of the SSE-induced ISHE voltage V appearing in the RuO2 film changes depending on the post annealing temperature, and that the magnitude can become larger than that of a standard SSE device using Pt. The similar sign change was also observed in Hall-resistance measurements of the RuO2 films. X-ray absorption fine structure (XAFS) spectra of as-deposited and annealed RuO2 revealed that the annealing process substantially improved the long-range crystalline order in RuO2. This suggests that change in the crystalline order may modify the dominant ISHE mechanism or electronic states in RuO2, leading to the sign reversal of V as well as the Hall coefficient. Our result demonstrates that RuO2 is an interesting material not only as a practical ISHE film but also as a testbed to study physics of spin-to-charge converters that depend on their crystalline order.