Rb-Doped Perovskite Oxides: Surface Enrichment and Structural Reconstruction During the Oxygen Evolution Reaction.

Alkali-metal doped perovskite oxides have emerged as promising materials due to their unique properties and broad applications in various fields, including photovoltaics and catalysis. Understanding the complex interplay between alkali metal doping, structural modifications, and their impact on performance remains a crucial challenge. In this study, this challenge is addressed by investigating the synthesis and properties of Rb-doped perovskite oxides. These results reveal that the doping of Rb into perovskite oxides function as a structural modifier in the as-synthesized samples and during the oxygen evolution reaction (OER) as well. Electron microscopy and first-principles calculations confirm the enrichment of Rb on the surface of the as-synthesized sample. Further investigations into the electrocatalytic reaction revealed that the Rb-doped perovskite underwent drastic restructuring with Rb leaching and formation of strontium oxide.

Distilling Accurate Descriptors from Multi-Source Experimental Data for Discovering Highly Active Perovskite OER Catalysts.

Perovskite oxides are promising catalysts for the oxygen evolution reaction, yet the huge chemical space remains largely unexplored due to the lack of effective approaches. Here, we report the distilling of accurate descriptors from multi-source experimental data for accelerated catalyst discovery by using the newly designed method of sign-constrained multi-task learning within the framework of sure independence screening and sparsifying operator that overcomes the challenge of data inconsistency between different sources. While many previous descriptors for the catalytic activity were proposed based on respective small data sets, we obtained a new 2D descriptor (dB, nB) based on 13 experimental data sets collected from different publications. Great universality and predictive accuracy, and the bulk-surface correspondence, of this descriptor have been demonstrated. With this descriptor, hundreds of unreported candidate perovskites with activity greater than the benchmark catalyst Ba0.5Sr0.5Co0.8Fe0.2O3 were identified from a large chemical space. Our experimental validations on five candidates confirmed the three highly active perovskite catalysts SrCo0.6Ni0.4O3, Rb0.1Sr0.9Co0.7Fe0.3O3, and Cs0.1Sr0.9Co0.4Fe0.6O3. This work provides an important new approach in dealing with inconsistent multi-source data for applications in the field of data-driven catalysis and beyond.