A further challenge for AI-guided OER discovery is to capture the evolving electrochemical interface. Martínez-Hincapié and co-workers showed that interfacial solvation can pre-organize the OER transition state and influence the kinetic regime [4]. Their kinetic analysis links a bias-dependent turning potential to changes in the relative importance of interfacial solvation and surface energetics: before the turning potential, excess charge and electric fields may organize the interfacial hydrogen-bond network and influence activation parameters, whereas beyond it the solvation pre-step becomes less limiting. The turning potential is largely independent of catalyst loading or surface area and can therefore inform intrinsic catalytic activity. Accordingly, Fig. 1f combines the general kinetic-map framework of Ref. [4] with an experimental kinetic map for OER on IrO_x nanoparticles in 0.1 M H₂SO₄. For AI models, these insights motivate condition-dependent representations that complement conventional static descriptors. Applied potential and electrolyte conditions can be treated explicitly as model inputs, while reconstruction state, phase fractions, dissolution behavior, kinetic parameters, and operando spectroscopic features can be encoded as multimodal or time-resolved descriptors. Interfacial solvation and electric-field effects could additionally be represented through simulation-derived descriptors or experimentally accessible proxies, enabling models to learn activity and stability as functions of the evolving catalyst state and operating conditions. Such hierarchical decision workflows could be incorporated into closed-loop platforms that combine theory/databases, AI models, robotic synthesis, high-throughput characterization, electrochemical testing, and feedback learning, enabling iterative optimization of catalysts that are active, synthesizable, and durable under bias (Fig. 1g). For water oxidation, the value of AI lies not simply in larger composition-performance datasets, but in connecting activity, stability, synthesis, and interfacial dynamics within an integrated discovery framework.