CAREER: Understanding the Role of Spin-Dynamics in the Formation of Magnetic Microstructure

Mississippi State University, Mississippi State MS

Investigators

Abstract

This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2). NONTECHNICAL SUMMARY This CAREER award supports basic research and education with an aim to understand the magnetic behavior of materials using computer simulations that model their properties at atomic length scales. Magnets are crucial materials that enable modern technologies from electric motors and generators to headphones. However, even the behavior of the most common magnet, iron, is difficult to understand at the atomic scale using existing computational modeling techniques. In this project, the PI and his students will develop new computational methods to accurately describe the coupled evolution of atomic spins and their arrangement within the material using tools of machine learning as applied to materials modeling. The methods will be applied to understanding phase transformations and their connection to magnetic properties in elemental iron and cobalt. The understanding and new knowledge gained from these studies can then be used to guide the development of new magnets by helping to select materials and determine the processes to make the most efficient magnets possible. This award also supports the PI's educational activities at the high school, undergraduate, and graduate levels, as well as community outreach, particularly among under-represented groups. In addition to training graduate and undergraduate students in the area of computational materials modeling, the PI will develop a course in machine learning theory and methods. While most people know that machine learning and magnetism are important, there are a number of misconceptions about what they are and how they work. The PI will work with local secondary schools and adult education groups to provide opportunities to learn more about these topics and help dispel some of the mystery around them. TECHNICAL SUMMARY This CAREER award supports theoretical and computational research with an aim to understand the interplay between magnetic moment and crystal coordination in determining the magnetic and microstructural properties of materials. Despite numerous attempts, classical molecular dynamics (MD) simulations have failed to adequately capture the behavior of magnetic materials. Even the well-known martensitic transformation of iron from ferrite to austinite is beyond state-of-the-art MD methods, as they are not able to accurately describe the magnetic moment as an environmentally dependent dynamic variable. In this project, the PI and his students will develop the methodology to accurately describe coupled spin and lattice dynamics at the MD scale and apply it to iron and cobalt, enabling a significant advance in the understanding, modeling, and development of magnetic materials. This will be accomplished by using machine learned artificial neural network based interatomic potentials which explicitly consider the spin of individual atoms as a degree of freedom. These potentials will be trained using extensive databases of density functional theory results and used to model phase boundaries in both iron and cobalt, monitoring the role of local magnetic moments in the energetics and stability of such boundaries, and the evolution of these boundaries as phase transformations occur within the material. This award also supports the PI's educational activities at the high school, undergraduate, and graduate levels, as well as community outreach, particularly among under-represented groups. In addition to training graduate and undergraduate students in the area of computational materials modeling, the PI will develop a course in machine learning theory and methods. While most people know that machine learning and magnetism are important, there are a number of misconceptions about what they are and how they work. The PI will work with local secondary schools and adult education groups to provide opportunities to learn more about these topics and help dispel some of the mystery around them. 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.