  Binary stars undergo a variety of interactions and evolutionary phases,
critical for predicting and explaining observed properties. Binary population
synthesis with full stellar-structure and evolution simulations are
computationally expensive requiring a large number of mass-transfer sequences.
The recently developed binary population synthesis code POSYDON incorporates
grids of MESA binary star simulations which are then interpolated to model
large-scale populations of massive binaries. The traditional method of
computing a high-density rectilinear grid of simulations is not scalable for
higher-dimension grids, accounting for a range of metallicities, rotation, and
eccentricity. We present a new active learning algorithm, psy-cris, which uses
machine learning in the data-gathering process to adaptively and iteratively
select targeted simulations to run, resulting in a custom, high-performance
training set. We test psy-cris on a toy problem and find the resulting training
sets require fewer simulations for accurate classification and regression than
either regular or randomly sampled grids. We further apply psy-cris to the
target problem of building a dynamic grid of MESA simulations, and we
demonstrate that, even without fine tuning, a simulation set of only $\sim 1/4$
the size of a rectilinear grid is sufficient to achieve the same classification
accuracy. We anticipate further gains when algorithmic parameters are optimized
for the targeted application. We find that optimizing for classification only
may lead to performance losses in regression, and vice versa. Lowering the
computational cost of producing grids will enable future versions of POSYDON to
cover more input parameters while preserving interpolation accuracies.
