Abstract

Exploring the Synergy Between a priori Computational Intelligence and Flow Process Development

Jean-Christophe Monbaliu, Associate Professor, Center for Integrated Technology and Organic Synthesis, University of Liège

The assets of flow chemistry for (re)exploring forbidden chemistries and new process windows are now well established. However, when experimental data is lacking, developing a flow process can sometimes be cumbersome, time- and resource-intensive. This talk illustrates our efforts to lessen the experimental burden and to accelerate the development of new reactions and flow processes. Our work lies at the interface of computational chemistry and flow organic chemistry, and converges toward a quantum assistant that merges computational chemistry and machine learning. This predictive model scouts for the vast and new chemical space that flow chemistry provides. Not only will this assistant help you to decide whether your reaction is doable in flow, but it also provides the best conditions for the preparation of libraries within minutes.