Jiang Xue, Wang Yong, Jia Baorui, Qu Xuanhui, Qin Mingli
Beijing Advanced Innovation Center for Materials Genome Engineering, Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China.
Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China.
ACS Appl Mater Interfaces. 2022 Sep 14;14(36):41141-41148. doi: 10.1021/acsami.2c13435. Epub 2022 Aug 31.
Electrocatalytic water splitting is an attractive way to generate hydrogen and oxygen for obtaining clean energy. Oxygen evolution reaction (OER), as one of the half reactions of oxygen evolution, is kinetically unfavorable involving the transfer of four electrons. Hydroxides are promising candidates for efficient OER electrocatalysts toward water splitting because of their high intrinsic activity and active surface area. However, quantitative prediction of hydroxide electrocatalytic performances from high-dimensional component spaces remains a challenge, severely hindering the performance-oriented precise composition and process design. Herein, we introduce a machine learning-based OER activity prediction method for hydroxide catalysts under extensive doping space for the first time. The relationship among composition, morphology, phase, pH value of the electrolyte, type of the working electrode, and overpotential was successfully fitted by the random forest algorithm. The model shows a good precision on the forecast of new experiments with a mean relative error of 4.74%. Furthermore, a new high-activity hydroxide catalyst NiFeLa was rationally designed and experimentally prepared, showing an ultra-low OP of 226 mV for a current density of 10 mA cm. This work provides an effective and novel way for hydroxide electrocatalyst prediction, which can further enhance the electrocatalyst design toward high catalytic performance.
电催化水分解是一种极具吸引力的获取清洁能源的制氢和制氧方法。析氧反应（OER）作为析氧的半反应之一，在动力学上是不利的，涉及四个电子的转移。氢氧化物因其高本征活性和活性表面积，是用于高效析氧电催化水分解的有前景的候选材料。然而，从高维成分空间对氢氧化物电催化性能进行定量预测仍然是一个挑战，严重阻碍了以性能为导向的精确成分和工艺设计。在此，我们首次引入了一种基于机器学习的在广泛掺杂空间下的氢氧化物催化剂析氧活性预测方法。通过随机森林算法成功拟合了成分、形态、相、电解液pH值、工作电极类型和过电位之间的关系。该模型对新实验的预测具有良好的精度，平均相对误差为4.74%。此外，合理设计并通过实验制备了一种新型高活性氢氧化物催化剂NiFeLa，在电流密度为10 mA cm时显示出226 mV的超低过电位。这项工作为氢氧化物电催化剂预测提供了一种有效且新颖的方法，可进一步推动高催化性能电催化剂的设计。
