  Alloys present the great potential in catalysis because of their adjustable
compositions, structures and element distributions, which unfortunately also
limit the fast screening of the potential alloy catalysts. Machine learning
methods are able to tackle the multi-variable issues but still cannot yet
predict the complex alloy catalysts from the properties of pure metals due to
the lack of universal descriptors. Herein we propose a transferable
machine-learning model based on the intrinsic properties of substrates and
adsorbates, which can predict the adsorption energies of single-atom alloys
(SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by
training the properties of transition metals (TMs). Furthermore, this model
builds the structure-activity relationship of the adsorption energies on alloys
from the perspective of machine learning, which reveals the role of the surface
atoms' valence, electronegativity and coordination and the adsorbates' valence
in determining the adsorption energies. This transferable scheme advances the
understanding of the adsorption mechanism on alloys and the rapid design of
alloy catalysts.
