Machine learning the relationship between Debye temperature and superconducting transition temperature

Recently a relationship between the Debye temperature $\Theta_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship indicates that $T_c \le A \Theta_D$ for phonon-mediated BCS superconductors, with $A$ being a pre-factor of order $\sim 0.1$. In order to verify this bound, we train machine learning (ML) models with 10,330 samples in the Materials Project database to predict $\Theta_D$. By applying our ML models to 9,860 known superconductors in the NIMS SuperCon database, we find that the conventional superconductors in the database indeed follow the proposed bound. We also perform first-principles phonon calculations for H$_{3}$S and LaH$_{10}$ at 200 GPa. The calculation results indicate that these high-pressure hydrides essentially saturate the bound of $T_c$ versus $\Theta_D$.Comment: 10 pages, 5 figure

Deposition of Nanostructured Thin Film from Size-Classified Nanoparticles

Materials comprising nanometer-sized grains (approximately 1_50 nm) exhibit properties dramatically different from those of their homogeneous and uniform counterparts. These properties vary with size, shape, and composition of nanoscale grains. Thus, nanoparticles may be used as building blocks to engineer tailor-made artificial materials with desired properties, such as non-linear optical absorption, tunable light emission, charge-storage behavior, selective catalytic activity, and countless other characteristics. This bottom-up engineering approach requires exquisite control over nanoparticle size, shape, and composition. We describe the design and characterization of an aerosol system conceived for the deposition of size classified nanoparticles whose performance is consistent with these strict demands. A nanoparticle aerosol is generated by laser ablation and sorted according to size using a differential mobility analyzer. Nanoparticles within a chosen window of sizes (e.g., (8.0 plus or minus 0.6) nm) are deposited electrostatically on a surface forming a film of the desired material. The system allows the assembly and engineering of thin films using size-classified nanoparticles as building blocks

Crystallographic texture in pulsed laser deposited hydroxyapatite bioceramic coatings

The orientation texture of pulsed laser deposited hydroxyapatite coatings was studied by X-ray diffraction techniques. Increasing the laser energy density of the KrF excimer laser used in the deposition process from 5 to 7 J/cm2 increases the tendency for the c-axes of the hydroxyapatite grains to be aligned perpendicular to the substrate. This preferred orientation is most pronounced when the incidence direction of the plume is normal to the substrate. Orientation texture of the hydroxyapatite grains in the coatings is associated with the highly directional and energetic nature of the ablation plume. Anisotropic stresses, transport of hydroxyl groups and dehydroxylation effects during deposition all seem to play important roles in the texture development.close252

Roadmap on artificial intelligence and big data techniques for superconductivity

This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10–20 yr time-frame