High Throughput Experimentation

Exploration of the chemical space in heterogeneous catalysis as well as optimization of reaction conditions remains one of the most time and cost consuming aspects of chemistry research. In contrast to singly executed experiments, high-throughput experimentation enables us to rapidly and systematically screen different catalyst compositions and reaction conditions while allowing room for serendipitous discovery. Furthermore, the large amount of data generated in a short time frame allows us to identify and rationalize trends as well as generate new guidelines for informed next-generation catalyst design. With the help of cutting-edge automation tools members of the group were able to streamline the discovery and optimization work for challenging reactions.

Using chemical descriptors, we developed a molecular understanding of ligand effects in heterogeneous olefin metathesis catalysts [1]. For semi-hydrogenation, we optimized the ligands needed to obtain higher selectivity to Z-alkenes when using catalytic Cu nanoparticles [2]. In homogeneous catalysis we studied noncovalent interactions across varying ligands in Mo olefin metathesis catalysts and studied the steric and electronic properties of ligands in Pd catalyzed cyanation [3,4]. We are then able to discover highly productive catalyst compositions and/or correlate specific chemical descriptors with increased activity. More recently, we conducted a data-driven HTE screening of metal compositions used for CO2 hydrogenation to accelerate catalyst discovery and optimization [6].

Coupled with data science approaches, we are able to not only improve the discovery of active catalysts but rationalize structure activity relationships by the identification of molecular and physico-chemical descriptors for reactivity. At the Copéret group, we collaborate closely with SwissCat+ and external page NCCR Catalysis with state-of-the-art infrastructure and expertise[6,7].