  The recent boom in computational chemistry has enabled several projects aimed
at discovering useful materials or catalysts. We acknowledge and address two
recurring issues in the field of computational catalyst discovery. First,
calculating macro-scale catalyst properties is not straight-forward when using
ensembles of atomic-scale calculations (e.g., density functional theory). We
attempt to address this issue by creating a multiscale model that estimates
bulk catalyst activity using adsorption energy predictions from both density
functional theory and machine learning models. The second issue is that many
catalyst discovery efforts seek to optimize catalyst properties, but
optimization is an inherently exploitative objective that is in tension with
the explorative nature of early-stage discovery projects. In other words: why
invest so much time finding a "best" catalyst when it is likely to fail for
some other, unforeseen problem? We address this issue by relaxing the catalyst
discovery goal into a classification problem: "What is the set of catalysts
that is worth testing experimentally?" Here we present a catalyst discovery
method called myopic multiscale sampling, which combines multiscale modeling
with automated selection of density functional theory calculations. It is an
active classification strategy that seeks to classify catalysts as "worth
investigating" or "not worth investigating" experimentally. Our results show a
~7-16 times speedup in catalyst classification relative to random sampling.
These results were based on offline simulations of our algorithm on two
different datasets: a larger, synthesized dataset and a smaller, real dataset.
