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Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL’s performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL’s effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability. 

Abstract Magnetic topological materials have recently emerged as a promising platform for studying quantum geometry by the nonlinear transport in thin film devices. In this work, an antiferromagnetic (AFM) semiconductor EuSc₂Te₄ as the first bulk crystal that exhibits quantum geometry‐driven nonlinear transport is reported. This material crystallizes into an orthorhombic lattice with AFM order below 5.2 K and a bandgap of less than 50 meV. The calculated band structure aligns with the angle‐resolved photoemission spectroscopy spectrum. The AFM order preserves combined space‐time inversion symmetry but breaks both spatial inversion and time‐reversal symmetry, leading to the nonlinear Hall effect (NLHE). Nonlinear Hall voltage measured in bulk crystals appears at zero field, peaks near the spin‐flop transition as the field increases, and then diminishes as the spin moments align into a ferromagnetic order. This field dependence, along with the scaling analysis of the nonlinear Hall conductivity, suggests that the NLHE of EuSc₂Te₄ involves contributions from quantum metric, in addition to extrinsic contributions, such as spin scattering and junction effects. Furthermore, this NLHE is found to have the functionality of broadband frequency mixing, indicating its potential applications in electronics. This work reveals a new avenue for studying magnetism‐induced nonlinear transport in magnetic materials.