Magnetic Recording Media based on High Entropy Alloys

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Areal density in magnetic recording media has enjoyed tremendous growth in recent decades. A key property that enables digital information to be stored in ever shrinking nanoscale magnets is the ability of the media to maintain its magnetization, known as the magnetic anisotropy. Traditional high magnetic anisotropy materials contain rare-earth elements, which are prone to price fluctuations and supply chain issues. Currently, certain alloys of FePt in a tetragonal phase are being pursued in heat-assisted magnetic recording media, but they contain precious metals. It is critical to advance the magnetic recording technology using high anisotropy materials based on earth-abundant elements. Recent progresses in high entropy alloys offer an exciting new arena to realize novel types of high anisotropy materials. These materials are multi-element alloys with high configurational entropy, allowing stabilization of metastable phases. This project will explore the high entropy alloy approach to develop novel multi-element phases with high magnetic anisotropy as magnetic recording media that cannot be achieved by conventional means. To effectively explore the essentially infinite combinations of the multi-element alloys, an iterative synthesis, characterization, simulation and machine learning approach is adapted, to quickly identify the most promising alloy phases and improve their performance metrics. Magnetic recording media developed using high entropy alloys have potentially transformative technological impacts. The effective machine learning method developed will benefit not only explorations of high entropy alloys, but also numerous other applications. The principal investigators actively promote broader participation through education and outreach efforts to engage students at all levels, as well as extensive service activities in the magnetism community. High entropy materials with strong magnetic anisotropy are investigated for applications as magnetic recording media. A synergetic and iterative high-throughput approach is employed, to survey the vast parameter space and expedite the research progress towards recording media development. Thin films of high entropy alloys are synthesized via combinatorial fabrication, exploring large composition variations. Structural characterizations of crystal structure, phase stability and morphology are performed by x-ray diffraction and electron microscopy. Quantitative magnetic phase identification and magnetization reversal characteristics are investigated using magnetometry and the first-order reversal curve method. Magnetic correlation length scales are probed by neutron scattering and compared with sample microstructures. Machine learning is employed to aid design and optimization of alloy composition and growth parameter space, forming iterative feedback loops with experiments. This includes the development of predictive machine learning using conventional models and existing data, training a Wasserstein Generative Adversarial Network (WGAN) to resolve the overfitting issue, searching for the maximized magnetic anisotropy, and realizing machine-learning-assisted high throughput characterization. An experiment-machine learning iteratively derived prototype magnetic recording media is demonstrated. A key bottleneck of stabilizing high anisotropy phases via the conventional approaches is circumvented via the configurational entropy route. The eventual scaling up from thin films into bulk materials can potentially transform many other industry sectors. A large-size high entropy alloy database containing both the experimental and WGAN-generated labeled data is publicly available. The research activities are integrated with a wide variety of education and outreach efforts for student training and broadening participation from underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.