Owing to their outstanding high-temperature resistance, oxidation resistance, radiation tolerance, and corrosion resistance, silicon carbide fiber-reinforced silicon carbide (SiC_f/SiC) composites have shown extensive potential in advanced applications, including aerospace and nuclear industries. SiC_f/SiC composites are considered among the most promising accident-tolerant fuel cladding materials, especially in the context of fourth-generation fission reactor development, owing to their stability under extreme conditions. However, the complex processing and structural characteristics of the material leave room for further research, especially with the emergence of new technologies like artificial intelligence (AI). Therefore, this work will provide a review of various processes, including chemical vapor infiltration (CVI), polymer infiltration and pyrolysis (PIP), nano impregnation and transient eutectic method (NITE), and reactive melt infiltration (RMI), focusing on improving material density, mechanical properties, and irradiation stability. Additionally, an in-depth review of the mechanical properties and microstructural changes of SiC_f/SiC composites and their cladding components under extreme conditions, such as high temperatures, irradiation, and corrosion, is provided, as these factors directly affect their long-term stability in nuclear reactors. Notably, numerical simulation technology has become a crucial tool for predicting the service performance of materials. Integrating advanced technologies like AI is expected to further promote the application of SiC_f/SiC composites in future high-temperature structural materials. In summary, significant progress has been made in the study of SiC_f/SiC composites as next-generation nuclear fuel cladding materials. However, further research is needed in areas such as fabrication process optimization, interface modification, service behavior evaluation, and integration with AI to meet the higher performance demands of future nuclear energy systems.

The increasing global energy demand and the growing environmental problems have intensified the pursuit of clean and sustainable energy solutions. Hydrogen, with its high energy density and clean by-products, is a promising candidate as an energy source. Fuel cells play a key role in harnessing hydrogen energy, but this technology faces challenges such as the trade-off between material stability and ion conductivity, which limits its widespread application. To address these challenges, designing material properties and adjusting system parameters are highly desirable. However, the traditional trial-and-error approach is no longer feasible when dealing with the vast array of possibilities. Fortunately, the advancement of artificial intelligence (AI) offers a new approach which can dramatically speed up the material design and parameter control. This article reviews the application of AI in fuel cells, especially its ability to accelerate material development. The review begins by outlining the mechanisms and classifications of fuel cells, as well as the property requirements for each part of the fuel cells. Subsequently, the article introduces the basic concepts of AI and its application in materials science, including the workflows of data aggregation, feature construction, model training, and experimental validation. Importantly, the applications of AI in predicting fuel cell material performance are highly emphasized and discussed. In addition, the challenges encountered in AI applications are introduced, including sparse datasets, complex feature engineering, the limitations of general models, and the weak interpretability of AI models, along with their respective development blueprints.

Reinforcement learning (RL) is emerging as a powerful tool in materials science, delivering a paradigm shift in how we find and optimize high-dimensional chemical and structural spaces. Unlike traditional methods, RL agents are able to learn to explore complex energy landscapes in an adaptive manner, instantaneously making decisions that guide the discovery of novel materials with certain properties. However, the application of RL to materials discovery faces unique challenges, including data scarcity, computationally expensive, and the challenge of designing reward functions that can balance multiple material objectives optimally. In this review, the current challenges and difficulties in applying RLtechniques in materials science and recent advances combining RL with machine learning, generative models, and domain knowledge are emphasized. We also outline promising future directions, such as transfer learning, hybrid models, and the creation of collaborative, open-access data infrastructures. By addressing these challenges, RL has the potential to transform the discovery and design of functional materials for catalysis, energy storage, and sustainability applications.