Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

The
increasing urgency to make chemical processes more environmentally
friendly while continuing to derive the chemicals required for modern
society from renewable resources requires the development of a forthcoming
generation of synthetic processes and the catalysts needed to facilitate
these reactions. Recently, applications of machine-learning (ML) algorithms
involving catalysis have begun to appear with increasing frequency,
as they constitute an attractive pathway both for discovering prospective
species and identifying trends surrounding catalytic behavior, principally
because the number of potential catalysts that can be examined greatly
exceeds those found in more traditional experimental or theoretical
approaches. Here, we harness a data-driven approach powered by ML
in tandem with molecular volcano plots to estimate the activity of
over 143,000 homogeneous nickel catalysts bearing phosphine and N-heterocyclic
carbene ligands for the reductive C­(sp2)–O cleavage
reaction in aryl ether compounds, an important step in the degradation
of biomass (lignin) into industrially useful feedstock chemicals.
Our computational workflow reveals that a vast majority of Ni-phosphine
and Ni-carbene catalysts are not ideally tuned to facilitate this
reaction. An analysis of those species identified as being the most
promising uncovers a clear catalytic design strategy that can be exploited
in an experimental setting to enhance the rate of reductive C­(sp2)–O cleavage of aryl ether compounds.