This research proposes a high-throughput computational framework integrating DFT, machine learning, and advanced sampling algorithms to screen for non-collinear magnetism in kagome metal systems, aiming to accelerate discovery of novel materials for spintronics and quantum computing applications.
Key findings
Non-collinear magnetic order in kagome metals is a frontier in condensed matter physics.
The framework leverages magnetic structure prototypes and Berry curvature calculations to identify candidate materials.
The methodology aims to enable screening of thousands of compounds for enhanced anomalous Hall responses and high Néel temperatures.
Limitations & open questions
Computational expense of ab initio calculations and vast chemical space of possible kagome compounds.
Challenges in predicting magnetic ground states from first principles.