(1 of 25)development, it is still far from meeting the increased demand. [3] The emergence of artificial intelligence (AI) has fundamentally changed the situation, which has significantly accelerated the discovery process, owing to greatly improved algorithms and developments in data science. [4] Machine learning (ML), a simple and practical AI framework based on computer and statistical science, is used to develop algorithms to learn from historic data without being explicitly programmed to obtain specific results. [5] It can investigate relationships that are hard to clearly and definitely model mathematically, providing insights for new scientific advancements related to highly complex with many uncertain twisted together factors. [6] There are usually three factors that govern the learning and prediction process of an ML: algorithms, data/database, and descriptors. [4] The algorithms involve data extraction, data filing, and propagation from mathematical derivation. [5,7] The data can be derived not only from experiments but also from theoretical calculations. [4] A number of databases based on experiments [8] and calculations [9] have already been established. The descriptors depend to a large extent on the predicted material or properties. Based on the algorithm, databases, and descriptors, the ML applications have been successfully implemented to support various energy materials with analysis tools (e.g., Python-based SciKit-Learn [10] and TensorFlow [11] ) in combination with workflow management tools (e.g., ASE [12] and Atomate [13] ). However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [14] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability. [5,15] Thus, suitable descriptors must be established to understand the structure-activity relationship. Although many efforts have been made to accelerate the rational design of homogeneous catalysts, [7b,16] heterogeneous catalysts, [7b,16b,17] and electrocatalysis, [3,18] the development of ML-assisted real catalysts is still in its infancy. Despite these considerable research efforts, the lack of universal selection tactics for descriptors bridging the gap between activity and structures impedes the application

Pt–Co electrocatalysts have attracted significant attention because of their excellent performance in many electrochemical reactions. This review focuses on Pt–Co electrocatalysts designed and prepared for electrocatalytic applications. First, the various synthetic methods and synthesis mechanisms are systematically summarized; typical examples and core synthesis parameters are discussed for regulating the morphology and structure. Then, starting with the design and structure–activity relationship of catalysts, the research progress of the morphologies and structures of Pt–Co electrocatalysts obtained based on various strategies, the structure–activity relationship between them, and their properties are summarized. In addition, the important electrocatalytic applications and mechanisms of Pt–Co catalysts, including electrocatalytic oxidation/reduction and bifunctional catalytic reactions, are described and summarized, and their high catalytic activities are discussed on the basis of their mechanism and active sites. Moreover, the advanced electrochemical in situ characterization techniques are summarized, and the challenges and direction concerning the development of high‐performance Pt–Co catalysts in electrocatalysis are discussed.

Cesium (Cs+) and strontium (Sr2+) ions are the main fission byproducts in the reprocessing of spent nuclear fuels for nuclear power plants. Their long half-live period (30.17 years for 137Cs...