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

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Abstract

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 (d_B, n_B) 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 Ba_0.5Sr_0.5Co_0.8Fe_0.2O₃ were identified from a large chemical space. Our experimental validations on five candidates confirmed the three highly active perovskite catalysts SrCo_0.6Ni_0.4O₃, Rb_0.1Sr_0.9Co_0.7Fe_0.3O₃, and Cs_0.1Sr_0.9Co_0.4Fe_0.6O₃. 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.

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