Magnetic Recording Media based on High Entropy Alloys

基于高熵合金的磁记录介质

• 批准号: 2151809
• 负责人: Kai Liu
• 金额: $ 39万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151809&HistoricalAwards=false
• 关键词: Magnetic; Recording; Media; based; Entropy

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.

近几十年来，磁记录介质的面密度有了巨大的增长。使数字信息能够存储在不断缩小的纳米级磁体中的一个关键特性是介质保持其磁化的能力，即磁各向异性。传统的高磁各向异性材料含有稀土元素，容易受到价格波动和供应链问题的影响。目前，在热辅助磁记录介质中正在追求具有四方相的某些FePT合金，但它们包含贵金属。基于富地球元素的高各向异性材料磁记录技术的发展至关重要。高熵合金的最新进展为实现新型高各向异性材料提供了一个令人振奋的新领域。这些材料是多元素合金，具有高的组态熵，允许亚稳相的稳定。该项目将探索高熵合金方法，以开发具有高磁各向异性的新型多元相作为磁记录介质，这是传统方法无法实现的。为了有效地探索多元合金本质上的无限组合，采用迭代合成、表征、模拟和机器学习的方法来快速识别最有希望的合金相并改善其性能指标。使用高熵合金开发的磁记录介质具有潜在的变革性技术影响。所开发的有效的机器学习方法不仅有利于高熵合金的探索，而且还将有助于许多其他应用。首席调查人员积极促进更广泛的参与，通过教育和外展努力让各级学生参与进来，以及在磁学社区开展广泛的服务活动。研究了具有强磁各向异性的高熵材料作为磁记录介质的应用。采用协同迭代的高通量方法，考察了广阔的参数空间，加快了记录介质开发的研究进展。通过组合法制备了高熵合金薄膜，探索了大的成分变化。用X射线衍射仪和电子显微镜对其晶体结构、相稳定性和形貌进行了表征。利用磁测量和一阶反转曲线法研究了磁体的定量磁相识别和磁化反转特性。用中子散射法探测了磁相关长度尺度，并与样品的微观结构进行了比较。利用机器学习辅助设计和优化合金成分和生长参数空间，与实验形成迭代反馈回路。这包括使用传统模型和现有数据开发预测机器学习，训练Wasserstein生成性对抗网络(WGAN)来解决过度拟合问题，搜索最大化的磁各向异性，以及实现机器学习辅助的高通量表征。展示了一种实验-机器学习迭代衍生的磁记录介质原型。通过构型熵路径绕过了通过传统方法稳定高各向异性相的关键瓶颈。从薄膜到块状材料的最终规模可能会改变许多其他行业。一个包含实验数据和WGAN生成的标记数据的大型高熵合金数据库是公开可用的。研究活动与各种各样的教育和外展努力相结合，以培训学生和扩大代表不足群体的参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Magneto-Ionic Control of Spin Textures and Interfaces

• DOI: 10.1109/tmrc56419.2022.9918544
• 发表时间: 2022-08
• 期刊: 2022 IEEE 33rd Magnetic Recording Conference (TMRC)
• 作者: G. Chen;C. Ophus;P. Murray;C. J. Jensen;A. Quintana;M. Robertson;E. Burks;D. Gilbert;J. Malloy;D. Bhattacharya;Z. Chen;G. Yin;A. Schmid;Kai Liu

Probing antiferromagnetic coupling in magnetic insulator/metal heterostructures

• DOI: 10.1103/physrevmaterials.6.094418
• 发表时间: 2022-09
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: P. Quarterman;Yabin Fan;Zhijie Chen;C. J. Jensen;R. Chopdekar;D. Gilbert;M. Holtz;M. Stiles;J. Borchers;Kai Liu;Luqiao Liu;A. Grutter

3D Interconnected Magnetic Nanowire Networks as Potential Integrated Multistate Memristors

• DOI: 10.1021/acs.nanolett.2c03616
• 发表时间: 2022-12-08
• 期刊: NANO LETTERS
• 影响因子: 10.8
• 作者: Bhattacharya, Dhritiman;Chen, Zhijie;Liu, Kai
• 通讯作者: Liu, Kai

Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry

• DOI: 10.1103/physrevresearch.5.043012
• 发表时间: 2023-04
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Bradley J. Fugetta;Zhijie Chen;D. Bhattacharya;Kun Yue;Kai Liu;A. Liu;G. Yin

Nitrogen-Based Magneto-ionic Manipulation of Exchange Bias in CoFe/MnN Heterostructures

• DOI: 10.1021/acsnano.2c12702
• 发表时间: 2023-03
• 期刊: ACS Nano
• 影响因子: 17.1
• 作者: C. J. Jensen;A. Quintana;P. Quarterman;A. Grutter;P. Balakrishnan;Huairuo Zhang;A. Davydov;Xixiang Zhang;Kai Liu