Reframing Catalytic Cycles: Graph-Based Dynamic Reaction Networks with Real-Time Quantum Feedback

Most current catalytic models (e.g., Song et al., 2021; Noodleman et al., 2023) treat reaction pathways as static or pre-defined. This idea proposes representing the entire catalytic cycle as a mutable graph, where nodes (intermediates, states) and edges (elementary steps) can be dynamically updated as new quantum information becomes available (for instance, from ultrafast spectroscopy or ab initio molecular dynamics). Real-time feedback would allow the model to adaptively discover new pathways, identify bottlenecks, or even predict when the system might jump to an unexpected or rare pathway (see Ghebreamlak et al., 2024 for evidence of such behavior in Fe-S clusters). This reframing could provide a powerful tool for discovering novel reaction mechanisms, especially in systems with complex or fluctuating energy landscapes.

References:

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