  High entropy alloys offer a huge search space for new electrocatalysts.
Searching for a global property maximum in one quinary system could require,
depending on compositional resolution, the synthesis of up to 10E6 samples
which is impossible using conventional approaches. Co-sputtered materials
libraries address this challenge by synthesis of controlled composition
gradients of each element. However, even such a materials library covers less
than 1% of the composition space of a quinary system. We present a new strategy
using deposition source permutations optimized for highest improvement of the
covered new compositions. Using this approach, the composition space can be
sampled in different subsections allowing identification of the contribution of
individual elements and their combinations on electrochemical activity.
Unsupervised machine learning reveals that electrochemical activity is governed
by the complex interplay of chemical and structural factors. Out of 2394
measured compositions, a new highly active composition for the oxygen reduction
reaction around Ru17Rh5Pd19Ir29Pt30 was identified.
