Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Abstract

Iron-based high-temperature (high-Tc) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-Tc superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability. Iron-based superconductors are promising for uses like quantum computing and superstrong magnets. However, improving their superconducting properties is challenging. This study aimed to improve these properties in a specific superconductor, K-doped Ba122, using Bayesian optimization. The researchers made samples under different conditions and measured their superconducting properties to refine the process. Two large disk-shaped samples were made using the best processing conditions found from data-driven and researcher-driven methods. The superconducting properties of these samples, and their ability to act as magnets, were tested at low temperatures. The results showed significant improvements, proving the optimization process’s effectiveness and resulting in an iron-based superconducting magnet with unprecedented strength. The study concludes that machine learning, especially Bayesian optimization, can significantly advance high-performance superconducting materials development. This could lead to more efficient and powerful superconducting magnets for various uses. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the authors. The world’s strongest iron-based superconducting magnet has been manufactured. Machine learning using Bayesian optimization was employed to improve the superconducting properties of potassium-doped barium iron arsenide (Ba,K)Fe2As2. Two large disk-shaped samples were fabricated using common industrial processing techniques under the best conditions deduced from data- and researcher-driven methods. After magnetizing the samples, they could retain a magnetic field of 2.83 T as a quasi-permanent magnet, around 2.7 times the previous record, with decay rates less than −0.1 ppm/h, crucial for MRI scanners. The two approaches produced divergent microstructures, opening the door to redefining what makes for a superior superconducting material.