Rules and Goals to Replace Clockwork Automation

Automation and real-time reaction monitoring have revolutionized synthetic chemistry by enabling data-rich experimentation, leading to a comprehensive understanding of chemical processes. This talk emphasizes the significance of data-rich experimentation (DRE) in reaction process optimization and discovery and explores the potential of artificial intelligence (AI) and machine learning (ML) tools in enhancing automated real-time reaction monitoring.<br/> <br/>DRE focuses on extracting real-time reaction progress data to gain insights into reaction kinetics, intermediates, rate constants, and by-product reaction pathways. Automation plays a crucial role in enabling DRE by accurately capturing and analyzing reaction aliquots, processing complex analytical data, and executing precise reaction manipulations. This approach enhances decision-making capabilities, reduces optimization time and resource requirements, and facilitates the exploration of reaction mechanisms and dynamics.<br/> <br/>This presentation highlights the current paradigm of data-driven reaction investigation, which often relies on human interpretation. However, the integration of real-time monitoring data with AI and ML tools presents an opportunity to accelerate process optimization and reaction discovery. Real-time monitoring telemetry allows automated systems to receive critical feedback and adapt to variable circumstances, enabling error-free autonomous synthesis. ML-based predictive models and autonomous optimization platforms reduce the number of experiments needed and consider a broader range of reaction parameters beyond simple yield measurements.<br/> <br/>Real-time reaction monitoring provides comprehensive kinetic data that addresses issues of data integrity, bias, and oversimplification. It captures variations in reaction performance, identifies intermediates and by-products, and facilitates the classification of underlying reaction mechanisms. By combining automated data-gathering methods with AI and ML tools, synthetic chemistry can predict optimal conditions and discover new synthetic routes more efficiently.