By how much can closed-loop frameworks accelerate computational materials discovery?
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.