Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received
tremendous attention in recent
years as a tool to design catalysts and discover unique descriptors
that capture the relationships between chemical properties and catalytic
performance. One of the stop-gaps in understanding catalytic effects,
which is often ignored and limits the deployment of data science tools,
relates to the lack of uniform data. The catalytic cleavage of C–X
(X= H, C, N, and O) bonds is relevant to many fundamental catalytic
processes. In this Perspective, we performed data analytics on four
groups of C–X cleavage reactions that are common in production,
upcycling, or reactive separation: the C–C cleavage in cyclopropyl
alcohol, the C–H cleavage in hydroacylation reactions, the
C–O cleavage in β-O-4 linkages, and the C–N cleavage
in amides, using experimental data collected from the literature to
understand their underlying correlations. Experimental variables of
high impact are identified for each reaction by dimensionality reduction
methods. We highlight the urgent need for experimental data sets that
include full details on the reaction conditions, such as reagent concentration,
reaction temperature, or time in machine-readable forms. We discuss
the potential improvement of the data of these reactions and promising
approaches such as autonomous experiments to fill the gaps in unbiased
experimental data. We also address the early stage consideration of
separation aspects in the experimental design of efficient catalytic
systems for these fundamental examples of chemical reactivity.