Nb${}_{\text{3}}$Sn is the original high-field superconductor but understanding its optimization in wire forms, which are always inhomogeneous, is compromised by a lack of basic understanding of its true property variations when it is alloyed with H_c${}_{\text{2}}$ enhancing additions (e.g., Hf, Ta, Ti, Zr). Since improving the high-field performance of Nb${}_{\text{3}}$Sn is important for many future applications in high energy physics, fusion, and medicine, we are studyingbulk binary and alloyed samples fabricated by ball milling itsconstituent powders and reacting them in a Hot Isostatic Press at pressures up to 2000 bar and temperatures up to 1800 °C. Remarkably, we found that these bulk samples, reacted to produce the A15 phase at temperatures between 1200 and 1800 °C, show minority traces of H_c${}_{\text{2}}$ (0 K), evaluated with a 99% criterion, that reach almost 40 T, more than 27% higher than the best value previously reported for Nb${}_{\text{3}}$Sn. These results are both scientifically interesting but potentially of great practical importance if they can be exploited for improving the high-field properties of Nb${}_{\text{3}}$Sn wires. One caution is that these samples do not have added Cu as wires do and that the high H_c${}_{{2}}$ reactions were all done at well above 1000 °C.

Magnetic drug targeting in stenosed arteries is affected by the local magnetic field distribution and the unsteady nature of blood flow. In this work, a two-dimensional finite element model was developed for a bifurcating artery with a 40% stenosis to examine the transport of 100 nm paramagnetic Fe2O3 carriers under pulsatile flow. The model couples the blood flow field, an externally applied magnetic field, and convection-diffusion transport with magnetophoretic migration. Pulsation frequencies of 1 and 2 Hz and magnetic fields of 0-0.50 T were considered. At 0.50 T, the maximum value of (B·∇)B in the target region was approximately 0.62×103T2/m. The carrier concentration at the stenosis increased from 0.63 to approximately 0.89 mol/m3 after 10 s at 1 Hz, whereas a final concentration of only about 0.65 mol/m3 was obtained at 2 Hz. The corresponding concentration enhancements were approximately 41% and 3%, respectively. Increasing the magnetic field also increased carrier accumulation: the concentration remained close to 0.63 mol/m3 at 0 T and reached approximately 0.695 and 0.89 mol/m3 at 0.25 and 0.50 T, respectively. The results indicate that magnetic carrier localization in a stenosed bifurcation is controlled by the competition between flow-induced convection and magnetophoretic migration, with stronger accumulation obtained at lower pulsation frequency and higher magnetic field strength.

High-entropy alloys (HEAs) extend alloy design beyond conventional solvent-based systems into vast multicomponent compositional spaces, enabling unusual combinations of structural and functional properties. This review connects the physical metallurgy of HEAs with the data-driven approaches increasingly used to accelerate their design and discovery. First, the thermodynamic and kinetic foundations of HEAs are examined, including configurational entropy, lattice distortion, diffusion behavior, and synergistic elemental interactions, while also considering the limitations of the classical core-effect framework. Liquid-state, solid-state, vapor-state, and emerging synthesis routes are then reviewed, with emphasis on how processing controls phase formation, chemical homogeneity, microstructure, defects, and contamination. The resulting mechanical, corrosion, thermal, electrical, and magnetic properties are discussed through processing–structure–property relationships. The second part evaluates artificial-intelligence and machine-learning approaches for phase classification, property prediction, interpretable modeling, active learning, Bayesian optimization, CALPHAD-assisted screening, and machine-learned interatomic potentials. Particular attention is given to the ability of these methods to reduce the experimental burden associated with the enormous HEA design space, as well as to persistent challenges involving data scarcity, class imbalance, inconsistent measurements, and inadequate representation of processing history and microstructure. Finally, emerging opportunities in transfer learning, generative models, physics-informed learning, and self-driving laboratories are discussed. By integrating physical metallurgy with data-driven design, this review provides a unified perspective on the transition from empirical alloy development toward accelerated HEA discovery.

High-entropy alloys (HEAs) are characterized by having more than one principal element, in contrast to a traditional alloy with only one base element; this provides a much wider composition space compared to traditional alloys; hence, HEAs have been the most studied metal alloys in the last couple of decades. This review links the fundamentals of these materials to the data-driven methods now used to design them. The first part of this review describes the fundamentals of these materials, including the thermodynamic and kinetic basis of these materials and an entropy-based classification. The subsequent parts of this review describe the synthesis routes of these materials via liquid-state, solid-state, gas-state, and other processing routes. The choice of processing route can have a significant impact on the resulting material, including the phase(s) formed, the chemical homogeneity, and contamination of the material. We have systematically reviewed the mechanical, corrosion, thermal, electrical, and soft magnetic properties of these HEAs. In the final section, we discuss how artificial intelligence (AI) and machine learning (ML) can be used to solve the problem of combinatorial explosion of the design space of HEAs: supervised classifiers for phase prediction, regression and ensemble models for hardness, strength, and elastic moduli, active learning and Bayesian optimization for closed-loop discovery, high-throughput screening with CALPHAD, and machine-learned interatomic potentials. We conclude with continuing constraints such as lack of data, noisy labels, and poor encoding of process history, and areas of development including transfer learning, generative design, and self-driving laboratories.