Backbone Diversity Analysis in Catalyst Design

Abstract

Abstractmagnified imageWe present a computer‐based heuristic framework for designing libraries of homogeneous catalysts. In this approach, a set of given bidentate ligand‐metal complexes is disassembled into key substructures (“building blocks”). These include metal atoms, ligating groups, backbone groups, and residue groups. The computer then rearranges these building blocks into a new library of virtual catalysts. We then tackle the practical problem of choosing a diverse subset of catalysts from this library for actual synthesis and testing. This is not trivial, since ‘catalyst diversity’ itself is a vague concept. Thus, we first define and quantify this diversity as the difference between key structural parameters (descriptors) of the catalysts, for the specific reaction at hand. Subsequently, we propose a method for choosing diverse sets of catalysts based on catalyst backbone selection, using weighted D‐optimal design. The computer selects catalysts with different backbones, where the difference is measured as a distance in the descriptors space. We show that choosing such a D‐optimal subset of backbones gives more diversity than a simple random sampling. The results are demonstrated experimentally in the nickel‐catalysed hydrocyanation of 3‐pentenenitrile to adiponitrile. Finally, the connection between backbone diversity and catalyst diversity, and the implications towards in silico catalysis design are discussed.