Autonomously exploring
chemical reaction networks with
first-principles
methods can generate vast data. Especially autonomous explorations
without tight constraints risk getting trapped in regions of reaction
networks that are not of interest. In many cases, these regions of
the networks are only exited once fully searched. Consequently, the
required human time for analysis and computer time for data generation
can make these investigations unfeasible. Here, we show how simple
reaction templates can facilitate the transfer of chemical knowledge
from expert input or existing data into new explorations. This process
significantly accelerates reaction network explorations and improves
cost-effectiveness. We discuss the definition of the reaction templates
and their generation based on molecular graphs. The resulting simple
filtering mechanism for autonomous reaction network investigations
is exemplified with a polymerization reaction.