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Abstract Magnetic high entropy alloys (HEAs) consisting of 3dtransition metals offer an exciting platform to explore novel magnetic phases as they often house competing exchange interactions in combination with random site disorders. In this work, a sensitive and tunable magnetic order is demonstrated in sputtered single‐layer FeCoNiMnAl_xfilms, as a function of non‐magnetic Al addition, along with an unexpected exchange bias effect. Thin films of 50 nm FeCoNiMn exhibit a face‐centered‐cubic (fcc) phase, reentrant spin glass (SG) transition near 100 K, and a large exchange bias of over 500 Oe after field‐cooling to 5 K. The exchange bias is increased to 930 Oe through a small addition of 5 at.% Al. Further Al addition to 12 at.% results in a body‐centered‐cubic (bcc) phase, coinciding with a large increase in the saturation magnetization, decrease of exchange bias to 50 Oe, and suppression of SG behavior. The change in magnetic order across the Al‐induced structural transformation is mediated by the switching of Mn ground state from AF to FM, which is supported by first‐principles calculations and experimentally confirmed via X‐ray magnetic circular dichroism. These results open up new HEA strategies for explorations of novel magnetic phases. 

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.