Design a site like this with
Get started

Machine Learning

Redox switchable systems are a great avenue to develop tools to inform catalytic reactions because the experimental work has to balance subtle effects in order to alter two orthogonal catalytic cycles. Due to the modularity of ferrocene-based ligand syntheses, a plethora of pre-catalyst combinations can be generated allowing to fine-tune the polymerization activity. In order to select the combinations that show the desired catalytic activity, we need to achieve a predictive tool that could be utilized for specific processes. While control of selectivity can be achieved, there is a need for rational design of other catalyst systems that circumvents trial and error. Therefore, we have embarked on a synergistic program that combines intimately computational and experimental methods with the ultimate goal of being able to predict catalyst design.

We recently used DFT calculations to capture the properties of redox-switchable metal complexes relevant to the ring-opening polymerization of cyclic esters by varying the metals, donors, linkers, and substituents in both accessible ferrocene oxidation states (Chem. Commun. 2019, 5587). A map of this chemical space highlights that modifying the ligand architecture and the metal has a larger impact on structural changes than changing the oxidation state of the ferrocene backbone.