CAREER: Advancing nanostructure & interface science for permanent magnets without rare earth materials

该奖项支持理论、计算和教育活动，旨在促进对磁场辅助制造过程中多组分永磁合金纳米结构形成机制的基本理解。这一知识为开发一种新型永磁体铺平了道路，这种永磁体由地球上丰富的元素组成，在高温下可以胜过最先进的永磁体。目前用于电动汽车和风力发电行业的高温永磁体是基于稀土元素的。在全球范围内，美国只生产一小部分工业用稀土元素，比如钕和镝。因此，开发它们的替代品可以减少美国对这些材料的依赖，并对美国的国家经济和能源安全产生积极影响。PI和他的团队将开发一个新的计算模型，通过结合新的人工智能方法来揭示可以改善一类由地球丰富元素（如铁、铝、镍和钴）制成的合金的理想磁性的机制。基于计算模型预测的化学和加工路线，将制造和表征一个概念验证永磁体。该奖项还为爱达荷州的高中生和大学生提供了一项独特的教育活动，通过新颖的教育课程和参与真实的机器学习项目，让他们获得机器学习的实践经验。该项目还将为高中教师提供专业培训。该项目的教育计划解决了科学、技术、工程和数学领域的国家和地区劳动力短缺问题，这主要是由于入学率和保留率低，特别是对得不到充分服务的学生。该奖项支持研究和教育活动，旨在发展对热磁处理过程中多组分永磁体合金纳米结构形成机制的基本理解。PI和他的团队将开发一个原子信息相场模型，以揭示fealnico基合金在热磁处理过程中纳米结构形成的机制，特别关注理解和设计富cu和富ni界面相，作为富feo相隔离的解决方案，从而改善磁性能。该团队还将开发一种新的混合数据三维卷积神经网络框架，用于构建fealnico基合金的工艺结构连接。一个基于神经网络预测的化学和加工路线的概念验证永磁体将被制造和表征。该项目的成果可能会取代稀土永磁体在高温应用中的应用，如电动汽车和风力发电机牵引电机，这是一种更广泛可用的替代品。该奖项还为爱达荷州的高中生和大学生提供了一项独特的教育活动，通过新颖的教育课程和参与真实的机器学习项目，让他们获得机器学习的实践经验。该项目还将为高中教师提供专业培训。该项目的教育计划解决了科学、技术、工程和数学领域的国家和地区劳动力短缺问题，这主要是由于入学率和保留率低，特别是对得不到充分服务的学生。该项目由材料研究部通过凝聚态物质和材料理论项目以及促进竞争研究的既定项目（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

NONTECHNICAL SUMMARYThis award supports theoretical, computational, and educational activities that aim to advance the fundamental understanding of mechanisms underlying nanostructure formation in multicomponent permanent magnet alloys during magnetic-field-assisted manufacturing. This knowledge paves the path toward developing a novel permanent magnet composed of earth-abundant elements that can outperform the state-of-the-art permanent magnets at high temperatures. Current high-temperature permanent magnets, used in electric vehicles and wind power production industries, are based on rare earth elements. The U.S. produces a small fraction globally of industrial rare-earth elements like neodymium and dysprosium. Therefore, developing alternatives to their use can reduce U.S. dependence on these materials and have a positive impact on U.S. national economic and energy security.The PI and his team will develop a new computational model by incorporating novel artificial intelligence methods to unravel the mechanisms that can improve the desirable magnetic properties in a class of alloys made of earth-abundant elements such as iron, aluminum, nickel, and cobalt. A proof-of-concept permanent magnet, based on the chemistry and processing routes predicted by computational modeling, will be fabricated and characterized. This award also supports a unique educational activity for Idaho high school and college students to gain hands-on experience on machine learning through a novel educational curriculum and involvement in authentic machine learning projects together. The project will also provide professional training for high school teachers. The education plan of the project addresses both national and regional workforce shortages in the areas of science, technology, engineering, and mathematics that primarily originate from low entrance and retention rates, particularly for underserved students.TECHNICAL SUMMARYThis award supports research and educational activities that aim to develop a fundamental understanding of mechanisms underlying nanostructure formation in multicomponent permanent magnets alloys during thermo-magnetic treatment. The PI and his team will develop an atomistically informed phase-field model to unravel the mechanism of nanostructure formation in FeAlNiCo-based alloys during thermo-magnetic treatment with a particular focus on understanding and engineering the Cu-rich and Ni-rich interfacial phases as solutions for FeCo-rich phase isolation and consequently magnetic property improvement. The team will also develop a new mixed-data three-dimensional convolutional neural network framework to construct the process-structure linkages for FeAlNiCo-based alloys. A proof-of-concept permanent magnet, based on the chemistry and processing routes predicted by the neural network, will be fabricated and characterized. The project outcomes can potentially displace rare earth-based permanent magnets in high-temperature applications, such as electric vehicle and wind generator traction motors, with a more widely available alternative.This award also supports a unique educational activity for Idaho high school and college students to gain hands-on experience on machine learning through a novel educational curriculum and involvement in authentic machine learning projects together. The project will also provide professional training for high school teachers. The education plan of the project addresses both national and regional workforce shortages in the areas of science, technology, engineering, and mathematics that primarily originate from low entrance and retention rates, particularly for underserved students.This project is jointly funded by the Division of Materials Research through the Condensed Matter and Materials Theory program, and the Established Program to Stimulate Competitive Research (EPSCoR).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.