Targeted evolution of pinning landscapes for large superconducting critical currents (1904.11120v1)

Abstract: The ability of type-II superconductors to carry large amounts of current at high magnetic fields is a key requirement for future design innovations in high-field magnets for accelerators and compact fusion reactors and largely depends on the vortex pinning landscape comprised of material defects. The complex interaction of vortices with defects that can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced by post-synthesis particle irradiation precludes a priori prediction of the critical current and can result in highly non-trivial effects on the critical current. Here, we borrow concepts from biological evolution to create a genetic algorithm evolving pinning landscapes to accommodate vortex pinning and determine the best possible configuration of inclusions for two different scenarios: an evolution process starting from a pristine system and one with pre-existing defects to demonstrate the potential for a post-processing approach to enhance critical currents. Furthermore, the presented approach is even more general and can be adapted to address various other targeted material optimization problems.

• The paper demonstrates using genetic algorithms to evolve optimal defect configurations in superconductors, identifying planar defects that achieve up to 40% of the depairing current.
• Evolved planar defects oriented along the current and field directions outperform traditional columnar or spherical pinning centers due to robust, collective vortex pinning.
• This computational approach offers practical implications for designing high-performance superconductors and theoretical strategies for complex materials science optimization problems.

Insights into the Targeted Evolution of Vortex Pinning Landscapes

The paper presents a computational exploration of the mechanisms for optimizing vortex pinning in type-II superconductors, employing concepts analogous to biological evolution. The core focus is the utilization of genetic algorithms to enhance the critical current densities ($J_c$) in the presence of a magnetic field by evolving artificial pinning landscapes. This methodology allows the identification of optimal defect configurations that can trap vortices more effectively and thereby maximize $J_c$.

Summary of Approach

In type-II superconductors, the interaction between magnetic flux vortices and non-superconducting defects significantly influences the critical current. Traditionally, predicting the most effective pinning landscape has been a challenge due to the complex interplay of vortices and defects. The authors propose borrowing from biological evolution, specifically through genetic algorithms, to iteratively refine and adapt the pinning landscapes for enhanced vortex pinning capabilities.

This approach is characterized by several stages:

1. Mutation and Selection: Starting with an initial configuration, defects are added, modified, or removed, and those configurations leading to higher $J_c$ are selected for further mutation.
2. Extrapolation and Analysis: The nature of the evolved defect distribution is analyzed to model and potentially extrapolate the optimal parameters.
3. Verification: Reintroducing the obtained configurations into the evolutionary loop verifies stability and optimality against further potential enhancements.

Key Results and Insights

Through targeted evolution, the paper identifies that optimal pinning can be achieved using configurations consisting of planar defects oriented along the current and magnetic field directions. These "evolved" pinning landscapes yielded critical currents up to 40% of the depairing current ($J_{dp}$) at low temperatures and moderate fields of $B = 0.1 H_{c2}$. This configuration was found to outperform traditional columnar and spherical pinning centers, which emphasize the unique advantage of such evolutionary methods in discovering high-performance configurations without preconceived notions of defect types.

The paper provides a systematic comparison between ordered columnar and planar arrays of defects. One noteworthy observation is the robustness of planar arrays against variations in defect parameters, indicating their suitability for diverse magnetic field strengths. Moreover, the findings stress that planar defects create a more effective collective vortex pinning environment compared to conventional columnar defects, especially in fields below their design field.

Practical and Theoretical Implications

The evolutionary algorithm's ability to enhance superconducting properties presents practical implications for the design of high-performance superconducting materials, particularly in the domains of magnets for particle accelerators and fusion reactors. The approach may guide the synthesis of superconductors optimized for specific operational environments, potentially reducing reliance on empirical trial-and-error methods.

Theoretically, the work underscores the potential of adaptive algorithms in exploring vast configuration spaces, offering a strategy to tackle high-dimensional optimization problems in materials science. This concept can potentially be adapted to other applications beyond superconductivity, wherever complex systems are involved.

Future Considerations

Future work might involve refining the genetic algorithm's constraints to incorporate more realistic manufacturing limits, such as material stability and defect formation energy considerations. Additionally, exploring more complex scenarios such as varying temperature fields or anisotropic magnetic fields could further enhance our understanding and capability to design tailored superconducting materials.

The broader adoption of such computational approaches could revolutionize material design, emphasizing the need for interdisciplinary collaboration between computer science, physics, and materials engineering in furthering the development of next-generation superconductors.

In conclusion, this paper demonstrates a powerful approach to optimizing material properties in superconductors, leveraging computational evolution to unveil configurations that were previously unattainable by conventional theoretical and experimental methods.