The development of highly efficient oxygen‐evolving catalysts compatible with powerful proton‐exchange‐membrane‐based electrolyzers in acid environments is of prime importance for sustainable hydrogen production. In this field, understanding the role of electronic structure of catalysts on catalytic activity is essential but still lacking. Herein, a family of pyrochlore oxides R2Ir2O7 (R = rare earth ions) is reported as acidic oxygen‐evolving catalysts with superior‐specific activities. More importantly, it is found that the intrinsic activity of this material significantly increases with the R ionic radius. Electronic structure studies reveal that the increased R ionic radius weakens electron correlations in these iridate oxides. This weakening induces an insulator–metal transition and an enhancement of IrO bond covalency, both of which promote oxygen evolution kinetics. This work demonstrates the importance of engineering the electron correlations to rationalize the catalytic activity toward water oxidation in strongly correlated transition‐metal oxides.

In article number https://doi.org/10.1002/adma.201805104, Shiming Zhou, Jie Zeng, and co‐workers develop pyrochlore‐type iridate oxides R2Ir2O7 (R = rare‐earth‐metal ions) as highly active and stable oxygen evolution reaction (OER) catalysts for acidic water oxidation, overwhelmingly superior to the benchmark IrO2 nanoparticles. The increase of the ionic radius of the R species strengthens hybridization between the Ir 5d and O 2p orbitals, which is responsible for the high OER activity.

Active learning (AL) aims to sample the most informative data instances for labeling, which makes the model fitting data efficient while significantly reducing the annotation cost. However, most existing AL models make a strong assumption that the annotated data instances are always assigned correct labels, which may not hold true in many practical settings. In this paper, we develop a theoretical framework to formally analyze the impact of noisy annotations and show that systematically re-sampling guarantees to reduce the noise rate, which can lead to improved generalization capability. More importantly, the theoretical framework demonstrates the key benefit of conducting active re-sampling on label-efficient learning, which is critical for AL. The theoretical results also suggest essential properties of an active re-sampling function with a fast convergence speed and guaranteed error reduction. This inspires us to design a novel spatial-temporal active re-sampling function by leveraging the important spatial and temporal properties of maximum-margin classifiers. Extensive experiments conducted on both synthetic and real-world data clearly demonstrate the effectiveness of the proposed active re-sampling function.