Catalyst discovery and, even, catalyst optimization is typically a trial-and-error process which can take years. Tuning of active metal, ancillary ligand framework, activator, and modifiers (if any) are often associated with non-additive effects, and intuition and common sense often meet their limits.

Combining High Throughput Experimentation (HTE) with deterministic and/or statistical computational modeling can shorten the timeframe for catalyst development and thus significantly reduce capital and resource expenditures by prioritizing targets for synthesis. At LSP we do that routinely, with highly efficient feedback loops. SPR (also known as Quantitative Structure Activity Relation, QSAR) models with heuristic potential rely on ‘massive’ HTE databases and computationally derived descriptors for the catalytically active species.