Catalysis and Computation - Better Together?

Computational studies of organometallic homogeneous catalysis play an increasingly important role in furthering (and changing) our understanding of catalytic cycles and can help to guide the discovery and evaluation of new catalysts (Dalton Trans., 2014, 43, 13545; Angew. Chem. Int. Ed. 2012, 51, 118). While a truly “rational design” process remains out of reach, detailed mechanistic information from both experiment and computation can be combined successfully with suitable parameters characterising catalysts and substrates to predict outcomes and guide screening (Chem. Asian J., 2014, 9, 1714-1723).

The computational inputs to this process rely on large databases of parameters characterising ligand and complex properties in a range of different environments (Organometallics, 2014, 33, 1751-1791; Organometallics, 2010, 29, 6245; ibid.2012, 31, 5302; Dalton Trans., 2013, 42, 172). Such maps of ligand space are combined with detailed computational mechanistic studies (Dalton Trans., 2014, 43, 13545; Dalton Trans., 2010, 39, 10833), using the best available computational approaches for synthetically-relevant complexes, on carefully selected subsets of ligands, as well as large-scale data analysis.

Rather than pursuing a purely computational solution of in silico catalyst design and evaluation, an iterative process of mechanistic study, data analysis, prediction and experimentation can accommodate complicated mechanistic manifolds and lead to useful predictions for the discovery and design of suitable catalysts. Recent work, in collaboration with the Lynam group, has demonstrated the potential of such a process (Organometallics, 2014, 33, 1751, Chem. Commun. 2015, 51, 9702), as well as helping us identify key challenges and bottlenecks to computational predictions. In this seminar, I will use examples drawn from recent work to illustrate this approach.