Mechanistic insights and rational design of emerging iron-based environmental catalysts for water remediation via...

Emerging iron-based catalysts have attracted increasing attention in water remediation, yet fully elucidating their underlying catalytic mechanisms remains challenging. Multiscale simulations have consequently become essential theoretical tools. Herein, we comprehensively review the recent advancements in applying multiscale computational strategies to emerging iron-based environmental catalysts. At the fundamental electronic scale, quantum chemical calculations and Density Functional Theory (DFT) are systematically utilized to elucidate spin-state transitions, charge-transfer kinetics, and specific oxidant activation pathways. Bridging the gap to mesoscopic scales, Molecular Dynamics (MD) simulations are employed to capture dynamic interfacial phenomena, including complex solvation structures, competitive adsorption, and pollutant diffusion within aqueous environments. Additionally, the transformative role of Machine Learning (ML) is highlighted, demonstrating how massive simulation datasets can be leveraged to extract intrinsic structural descriptors and accelerate high-throughput catalyst screening. By linking static electronic structures with dynamic interfacial behaviors, this review provides a robust theoretical framework for clarifying complex structure-activity relationships. Finally, current computational bottlenecks and future perspectives are discussed, emphasizing the synergistic integration of DFT, MD, and ML to advance the rational design of next-generation catalysts for environmental remediation.