SLAS 2019 AI in Process Automation Symposium

Optimizing multicomponent reactions is a challenge which lends itself well to automation due to the large number of experiments usually required. In addition to classic high throughput and Design of Experiment methodologies, the application of machine learning algorithms has recently come into focus; coupled with a robotic platform and on-line analytical tools, the ideal of a fully autonomous robotic researcher comes within reach. While historic examples of this concept have often been tailored to answer rather narrow questions, we herein present a powerful and versatile platform for interrogating a wide range of reaction systems.

Using an N9 robot by North Robotics as our central platform, we have developed a range of tools enabling us to formulate reactions, take samples, and perform on-line HPLC and MS/MS analysis, all under full automation. Results are passed on to ChemOS, a machine learning engine, which evaluates collected data and provides a new set of experimental conditions, generating a closed feedback loop enabling rapid optimization of complex reaction systems.

 To showcase the power of our platform, we have deployed several variations of this autonomous toolkit to tackle a diverse range of chemical applications. In this first demonstration, these include optimization of reaction parameters for catalytic transformations and discovery of champion thin-film materials for organic photovoltaic devices. Current progress and development will be discussed.