Researchers Hendrik Schrautzer, Tim Drevelow, Hannes Jónsson, and Pavel F. Bessarab, affiliated with the University of Iceland and the University of Luxembourg, have developed a computational framework to better understand the behavior of magnetic systems, particularly in two-dimensional chiral magnets. Their work, published in the journal Physical Review B, focuses on mapping out the energy landscape of these systems to reveal the mechanisms governing the transitions between different magnetic states.
The team’s framework systematically identifies ‘saddle points’—points of high energy that act as transition points between stable magnetic states. By understanding these transitions, researchers can gain insights into the behavior of magnetic textures, which are crucial for various applications in the energy sector, including data storage and spintronic devices.
The researchers’ method involves several stages. First, they identify the symmetry of a given minimum-energy configuration and use this to define subsystems. The eigenmodes (or natural vibrations) of these subsystems guide the system towards different saddle points. The geodesic minimum mode following method is then employed to efficiently converge onto these saddle points. Finally, the identified saddle points are embedded into a state network, providing a comprehensive map of accessible transitions and their associated energy barriers.
The researchers applied this framework to two-dimensional chiral magnets, revealing a hierarchy of transition mechanisms governing the nucleation, annihilation, and rearrangement of localized magnetic textures. They found that transitions can preserve the topological charge (a mathematical property related to the texture’s shape) or change it. By scaling the interaction parameters, they observed distinct behaviors for these two classes as the continuum limit is approached.
Interestingly, the researchers also found that textures with the same topological charge are not always connected by a homotopy (a continuous deformation) corresponding to a minimum-energy path. In specific parameter regimes, the total topological charge necessarily increases and then decreases (or vice versa) during the transition, returning to its initial value at the final state.
This research provides a powerful tool for understanding and controlling magnetic textures, which could lead to advancements in energy-efficient data storage and processing technologies. By mapping out the energy landscape of magnetic systems, researchers can design materials and devices with specific magnetic properties, paving the way for more efficient and sustainable energy solutions.
Source: Physical Review B, “Network of localized magnetic textures revealed using a saddle-point search framework”
This article is based on research available at arXiv.