Song Zhilong, Wang Xiao, Liu Fangting, Zhou Qionghua, Yin Wan-Jian, Wu Hao, Deng Weiqiao, Wang Jinlan
School of Physics, Southeast University, Nanjing, 211189, China.
Institute of Molecular Sciences and Engineering, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong, 266237, China.
Mater Horiz. 2023 May 9;10(5):1651-1660. doi: 10.1039/d3mh00157a.
Developing activity descriptors data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments. A universal activity descriptor to evaluate the oxygen evolution reaction (OER) performance of oxide perovskites free of calculations or experiments was constructed and reached high accuracy and generalization ability. Utilizing this descriptor with Bayesian-optimized parameters, a series of compelling double perovskites with excellent OER activity were predicted and further evaluated using first-principles calculations. Finally, the two ML-predicted nickel-based perovskites with the best OER activity were successfully synthesized and characterized experimentally. This work opens a new way to extend machine-learning material design by utilizing multiple data sources.
开发活性描述符 数据驱动的机器学习（ML）方法可以加速高活性和低成本电催化剂的设计。尽管文献中存储了大量电催化剂的活性数据，但由于不同的实验或计算条件，来自不同出版物的数据无法进行比较。在这项工作中，采用了一种可解释的ML方法——多任务符号回归，从多个实验的数据中进行学习。构建了一个通用的活性描述符，无需计算或实验即可评估钙钛矿氧化物的析氧反应（OER）性能，该描述符具有很高的准确性和泛化能力。利用该描述符和贝叶斯优化参数，预测了一系列具有优异OER活性的引人注目的双钙钛矿，并使用第一性原理计算进行了进一步评估。最后，成功合成了两种由ML预测的具有最佳OER活性的镍基钙钛矿，并进行了实验表征。这项工作通过利用多个数据源，为扩展机器学习材料设计开辟了一条新途径。
