  To date, density functional theory (DFT) is one of the most accurate and yet
practical theory to gain insight about materials properties. Although
successful, the computational cost is the main hurdle even today. A way out is
combining DFT with machine learning (ML) to reduce the computational cost
without compromising accuracy. However, the success of this approach hinges on
the correctness of the descriptors. In the present work, we demonstrate that,
based on {\it only} interatomic distances as descriptors, our ML model predicts
interaction energy between an adsorbate and Al cluster with absolute mean error
(AME) $\sim$ 0.05 eV (or less) and reproduces the PES experienced by an
incoming atom. Our extensive DFT calculations reveal that atoms experiencing
identical environment within a cluster have identical interaction energy
patterns. Further, we demonstrate that our model is not specific to Al
clusters, and could be applied to clusters of different elements as well. Its
application to compute PES experienced by various test atoms and molecules in
the vicinity of different clusters proves the transferability of the model not
just to clusters of different elements but also to various molecules. The
descriptors chosen are invariant to rotation, translation, and permutation yet
very simple to compute is one of the most crucial points of the present work.
