  The increased availability of computing time, in recent years, allows for
systematic high-throughput studies of material classes with the purpose of both
screening for materials with remarkable properties and understanding how
structural configuration and material composition affect macroscopic attributes
manifestation. However, when conducting systematic high-throughput studies, the
individual ab initio calculations' success depends on the quality of the chosen
input quantities. On a large scale, improving input parameters by trial and
error is neither efficient nor systematic. We present a systematic,
high-throughput compatible, and machine learning-based approach to improve the
input parameters optimized during a DFT computation or workflow. This approach
of integrating machine learning into a typical high-throughput workflow
demonstrates the advantages and necessary considerations for a systematic study
of magnetic multilayers of 3$d$ transition metal layers on FCC noble metal
substrates. For 6660 film systems, we were able to improve the overall success
rate of our high-throughput FLAPW-based structural relaxations from $64.8 \%$
to $94.3\ \%$ while at the same time requiring $17\ \%$ less computational time
for each successful relaxation.
