Core Insights - The integration of artificial intelligence (AI) and automation in synthetic chemistry is seen as the future, enhancing efficiency and reducing reliance on traditional trial-and-error methods [1][3][4] - The vastness of chemical space presents significant challenges for chemists, with the theoretical number of small molecules that can be synthesized reaching 10^60, far exceeding the number of stars in the universe [2][3] - Current methodologies in synthetic chemistry include "top-down" experimental approaches and "bottom-up" theoretical approaches, both facing efficiency and universality challenges, necessitating new tools [3][4] Group 1: Challenges in Synthetic Chemistry - Synthetic chemistry is fundamental for creating materials essential for agriculture, health, and industry, but faces increasing demands for new materials and performance [1][2] - The "top-down" approach relies on chemists' intuition and experience, while the "bottom-up" approach uses computational methods, both of which have limitations in efficiency and applicability [2][3] Group 2: Automation and AI in Research - Automation in laboratories, such as high-throughput technology, has been adopted to enhance efficiency in catalyst development, significantly reducing the time required for experiments [4][5] - The use of automated platforms allows researchers to design and test thousands of catalyst formulations quickly, leading to the discovery of new materials that would take much longer through traditional methods [5][6] Group 3: Future Directions - AI's role in chemistry is currently as a supportive tool rather than a replacement for human intuition, with significant potential for development in interpreting experimental results [6][8] - The concept of "self-driving laboratories" is emerging, where automated systems can analyze results and autonomously design subsequent experiments, creating a rapid iterative cycle of design, execution, and learning [9][10]