Materials Genome Institute, Shanghai University, Shanghai 200444, China.
Zhejiang Laboratory, Hangzhou 311100, China.
J Am Chem Soc. 2023 May 24;145(20):11457-11465. doi: 10.1021/jacs.3c03493. Epub 2023 May 9.
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 (, ) 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 BaSrCoFeO were identified from a large chemical space. Our experimental validations on five candidates confirmed the three highly active perovskite catalysts SrCoNiO, RbSrCoFeO, and CsSrCoFeO. 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.
钙钛矿氧化物是一种很有前途的析氧反应催化剂，但由于缺乏有效的方法，巨大的化学空间在很大程度上仍未得到探索。在这里，我们报告了一种新的方法，即通过在稳健独立筛选和稀疏算子框架内使用新设计的符号约束多任务学习，从多源实验数据中提取准确描述符，以加速催化剂的发现，从而克服了不同来源之间数据不一致的挑战。虽然以前有许多关于催化活性的描述符是基于各自的小数据集提出的，但我们基于从不同出版物收集的 13 个实验数据集获得了一个新的二维描述符（，）。该描述符具有很好的通用性和预测准确性，以及体相-表面一致性。利用该描述符，从一个大的化学空间中确定了数百种活性大于基准催化剂 BaSrCoFeO 的未报告的钙钛矿候选物。我们对五个候选物的实验验证证实了三种高活性的钙钛矿催化剂 SrCoNiO、RbSrCoFeO 和 CsSrCoFeO。这项工作为处理用于数据驱动催化等领域的多源数据不一致提供了一个重要的新方法。
