Search for: All records

ABSTRACT High magnetic anisotropy materials have critical applications in numerous technology sectors, largely relying on rare‐earth and precious metals which poses major sustainability challenges. The high entropy composition space offers a vast arena for exploration of high magnetic anisotropy materials based on earth‐abundant elements. However, common high entropy alloys favor disordered cubic crystal structures whereas ordered uniaxial structures are necessary for the desirable strong magnetic anisotropy. Here we report the discovery of novel quinary borides with C16 uniaxial crystal structure and high magnetic anisotropy. Switching the easy‐plane anisotropy of binary C16 borides to easy‐axis can be achieved through a suitable mixing of Fe and Co on the transition metal sublattice. Using a combinatorial sputtering approach, we explore the wider high entropy composition space to further enhance the anisotropy of the C16 phase by incorporation of additional magnetic 3dtransition metals. Significant coercivity increase, more than two‐fold, has been observed, compared with binary and ternary transition metal borides. Density functional theory calculations support the experimental findings, predicting anisotropy approaching 10⁷ erg/cm³, which is understood in terms of the optimized electronic structure of the high entropy borides. These results establish a promising boron‐assisted synthesis strategy to achieve strong magnetic anisotropy using earth‐abundant elements. 

Generative AI is generating much enthusiasm on potentially advancing biological design in computational biology. In this paper we take a somewhat contrarian view, arguing that a broader and deeper understanding of existing biological sequences is essential before undertaking the design of novel ones. We draw attention, for instance, to current protein function prediction methods which currently face significant limitations due to incomplete data and inherent challenges in defining and measuring function. We propose a “blue sky” vision centered on both comprehensive and precise annotation of existing protein and DNA sequences, aiming to develop a more complete and precise understanding of biological function. By contrasting recent studies that leverage generative AI for biological design with the pressing need for enhanced data annotation, we underscore the importance of prioritizing robust predictive models over premature generative efforts. We advocate for a strategic shift toward thorough sequence annotation and predictive understanding, laying a solid foundation for future advances in biological design.