Integrating automated experimentation with process analytical technology

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Over the past 5 years, I have positioned my research program at the forefront of organic chemistry automation. My interdisciplinary team now consists of 25 chemists, engineers, and computer scientists tackling challenges in kinetic analysis, online sampling of complex reaction mixtures, real-time crystallization monitoring, automated synthesis, and the application of artificial intelligence to optimize chemical processes. Our research goals center on developing automated analytical platforms to increase throughput and access data-rich experimentation. These systems can be integrated with robotic tools to execute chemical reactions and artificial intelligence software to "learn" from each iteration, enabling self-optimizing experimentation that accelerates discovery while freeing up researcher time. The labour-intensive process of obtaining analytical data on individual chemical systems is indispensable to fundamental mechanistic elucidation, pharmaceutical development, and process chemistry. Performing these studies manually, however, dramatically slows down development timelines as highly qualified researchers invest time on data collection rather than scientific innovation. My research program develops modular automated tools to gather real-time data across a variety of systems, including batch and continuous processes, air- or water-sensitive chemistry, and heterogeneous reactions that are incompatible with traditional sampling techniques. A key feature to our approach is the integration of process analytical technology (PAT) to provide cross-validated, data-rich information on the processes of interest with the smallest commitment of time and resources. Our suite of PAT includes high performance liquid chromatography, infrared spectroscopy, ion chromatography, nuclear magnetic resonance, advanced microscopy, and even computer vision. My research program aims to continue advancing these research areas to explore chemical synthesis, process optimization, and materials discovery. We will leverage automated online analytics in two main fields: the elucidation of catalytic mechanisms and the development of efficient crystallization procedures. The broader goal of establishing self-driving laboratories will then enable us to optimize chemical transformations and discover new materials at a vastly faster pace than traditional screening and manual experimentation methodologies.