We seek to develop a framework to design, explore, and control the emergent behavior in functional nanoscale heterostructures and deepen our understanding of the complex energy landscape that governs such behavior. Our focus is on confined heterostructures of nanomagnetic materials comprising ferromagnets and antiferromagnets. We aim to design and understand the formation of novel spin textures and their transport properties by controlling the ferroic interactions using (i) geometric parameters, such as patterned shapes, curvature, and interfaces, and (ii) microstructural parameters, such as defects, strain, and coupling between adjacent nanostructures. The specific challenges that we are addressing include:
- The effect of geometric confinement and curvature on spin textures in 2D and 3D nanostructures
- Emergent topological spin textures and their collective behavior in mixed-dimensional van der Waals (vdW) ferromagnetic heterostructures
- Magnetic frustration and solitonic transport in artificial spin ice networks
- Driven dynamics of magnetic nanostructures
- Domain behavior and transport in synthetic antiferromagnets
Our research approach utilizes in-situ 2D and 3D microscopy to visualize and understand the nanoscale behavior. We use a combination of aberration-corrected Lorentz transmission electron microscopy and advanced scanning force microscopy in order to address scientific challenges. We have developed in-situ techniques that allow us to visualize domain behavior, local structural and electronic environment, and transport behavior in nanostructures as a function of external stimuli, such as applied fields, temperature, and time. We combine these experiments with simulations and measurement of charge, potential, and current at the mesoscopic length scale. We also apply the microscopy and electronic transport measurement techniques to explore materials for applications in microelectronics, solid oxide fuel cells, ferroelectric memory, and quantum information science.
Our group also works on exploring novel materials that exhibit resistive switching phenomena for synaptic computing elements that can enable highly efficient neuromorphic computing architectures. We seek to understand the underlying atomic and electronic phenomena using our extensive expertise in materials synthesis and in-operando experiments.
More broadly, we interact with the wider imaging community at Argonne to develop experimental and computational capabilities for multi-modal imaging at the nanoscale, including incorporating machine learning approaches.