Group 1 - The core viewpoint of the articles is that the synthetic chemistry field is undergoing a significant paradigm shift from traditional "experience-driven" methods to "data-driven" and "intelligent-driven" approaches, primarily through the integration of robotics and artificial intelligence (AI) [1][2][4] - The introduction of high-throughput technology platforms allows researchers to systematically design thousands of different catalyst formulations and quickly identify patterns that would take traditional methods a significant amount of manpower to uncover, leading to the successful development of advanced materials [2][3] - The development of deep learning technologies has dramatically increased research efficiency, exemplified by Dow Chemical's use of Microsoft Azure AI, which reduced a 4-6 month workload to just 30 seconds, achieving a 200,000-fold increase in efficiency [3] Group 2 - Current advancements in AI models and automation technologies have significantly transformed the operational aspects of synthetic chemistry research, enabling high-precision, high-throughput, and safer chemical operations [4][5] - The first AI chemical robot, which autonomously completed 688 experiments in just 8 days, highlights the potential of AI in assisting rather than replacing chemists in experimental design [6][7] - Challenges such as data scarcity and the complexity of reactions remain in the application of AI in synthetic chemistry, necessitating a problem-driven approach that combines limited automation with human cognition for intelligent molecular creation [7][8]