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

NONTECHNICAL SUMMARY

This 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 SUMMARY

This 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.