  Autonomous computations that rely on automated reaction network elucidation
algorithms may pave the way to make computational catalysis on a par with
experimental research in the field. Several advantages of this approach are key
to catalysis: (i) Automation allows one to consider orders of magnitude more
structures in a systematic and open-ended fashion than what would be accessible
by manual inspection. Eventually, full resolution in terms of structural
varieties and conformations as well as with respect to the type and number of
potentially important elementary reaction steps (including decomposition
reactions that determine turnover numbers) may be achieved. (ii) Fast
electronic structure methods with uncertainty quantification warrant high
efficiency and reliability in order to not only deliver results quickly, but
also to allow for predictive work. (iii) A high degree of autonomy reduces the
amount of manual human work, processing errors, and human bias. Although being
inherently unbiased, it is still steerable with respect to specific regions of
an emerging network and with respect to the addition of new reactant species.
This allows for a high fidelity of the formalization of some catalytic process
and for surprising in silico discoveries. In this work, we first review the
state of the art in computational catalysis to embed autonomous explorations
into the general field from which it draws its ingredients. We then elaborate
on the specific conceptual issues that arise in the context of autonomous
computational procedures, some of which we discuss at an example catalytic
system.
