  Computational screening is indispensable for the efficient design of
high-entropy alloys (HEAs), which hold great potential for catalytic
applications. However, the chemical space of HEAs is exponentially vast with
respect to the number of constituent elements, and even screening calculations
using machine learning potentials can be enormously time-consuming. To address
this challenge, we propose a method to rapidly construct models that predict
the properties of HEAs from data on monometallic systems (or few-component
alloys). The core of our approach is a newly-introduced descriptor called local
surface energy ($LSE$), which reflects the local reactivity of solid surfaces
at atomic resolution. We successfully created a model using linear regression
to screen the adsorption energies of molecules on HEAs based on LSEs from
monometallic systems. Furthermore, we made high-precision model development by
employing both classical machine learning and quantum machine learning. Using
our method, we were able to complete the adsorption energy calculations of CO
molecules on 1000 patterns of quinary nanoparticles consisting of 201 atoms
within a few hours. These calculations would have taken hundreds of years and
hundreds of days using density functional theory and a neural network
potential, respectively. Our approach allows accelerated exploration of the
vast chemical space of HEAs facilitating the design of novel catalysts.
