  The implementation of automation and machine learning surrogatization within
closed-loop computational workflows is an increasingly popular approach to
accelerate materials discovery. However, the scale of the speedup associated
with this paradigm shift from traditional manual approaches remains an open
question. In this work, we rigorously quantify the acceleration from each of
the components within a closed-loop framework for material hypothesis
evaluation by identifying four distinct sources of speedup: (1) task
automation, (2) calculation runtime improvements, (3) sequential
learning-driven design space search, and (4) surrogatization of expensive
simulations with machine learning models. This is done using a time-keeping
ledger to record runs of automated software and corresponding manual
computational experiments within the context of electrocatalysis. From a
combination of the first three sources of acceleration, we estimate that
overall hypothesis evaluation time can be reduced by over 90%, i.e., achieving
a speedup of $\sim$$10\times$. Further, by introducing surrogatization into the
loop, we estimate that the design time can be reduced by over 95%, i.e.,
achieving a speedup of $\sim$$15$-$20\times$. Our findings present a clear
value proposition for utilizing closed-loop approaches for accelerating
materials discovery.
