标题： 二维碳氮化物研究的演变：将机器学习整合到传统方法中 显示英文标题

标题： Navigating the Evolution of Two-dimensional Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches

摘要： 碳氮化物研究在当今的研究努力中已达到一个有前景的阶段，由于其独特的电子和结构特性，它们在光催化、能量存储和传感等领域有着广泛的应用。 机器学习（ML）的最新进展为探索和优化这些材料的潜力开辟了新的途径。 本研究综述了机器学习技术在碳氮化物研究中的综合应用，并介绍了CN分类和最新进展。 我们讨论了所采用的方法，如监督学习、无监督学习和强化学习，在预测材料特性、优化合成条件和提高性能指标方面的应用。 关键发现表明，机器学习算法可以显著减少实验中的试错过程，加速发现过程，并提供对碳氮化物结构-性能关系的更深入见解。 强调了将机器学习与传统实验方法相结合的协同效应，展示了机器学习驱动模型成功预测具有增强功能特性的新型碳氮化物组成的案例。 还提出了该领域的未来方向，强调了高质量数据集、先进机器学习模型和跨学科合作的必要性，以充分实现碳氮化物材料在下一代技术中的潜力。 显示英文摘要

摘要： Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials. This study presents a comprehensive review of the integration of ML techniques in carbon nitride research with an introduction to CN classifications and recent advancements. We discuss the methodologies employed, such as supervised learning, unsupervised learning, and reinforcement learning, in predicting material properties, optimizing synthesis conditions, and enhancing performance metrics. Key findings indicate that ML algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, and provide deeper insights into the structure-property relationships of carbon nitride. The synergistic effect of combining ML with traditional experimental approaches is highlighted, showcasing studies where ML driven models have successfully predicted novel carbon nitride compositions with enhanced functional properties. Future directions in this field are also proposed, emphasizing the need for high-quality datasets, advanced ML models, and interdisciplinary collaborations to fully realize the potential of carbon nitride materials in next-generation technologies.