Artificial Intelligence-Driven Discovery of Magnetic Higher-Order Topological Corner States: A Review From Theoretical Framework to Large-Scale Screening
Abstract
This review explores the integration of artificial intelligence (AI) with condensed matter physics, specifically focusing on the prediction of magnetic higher-order topological corner states. We examine the theoretical foundations of higher-order topological insulators (HOTIs), the unique challenges posed by magnetic systems, and the application of equivariant graph neural networks in high-throughput screening. The article discusses active learning strategies for navigating vast chemical configuration spaces and addresses current limitations regarding data scarcity and strong electronic correlations.
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