  Molecular-level understanding of the interactions between the constituents of
an atomic structure is essential for designing novel materials in various
applications. This need goes beyond the basic knowledge of the number and types
of atoms, their chemical composition, and the character of the chemical
interactions. The bigger picture takes place on the quantum level which can be
addressed by using the Density-functional theory (DFT). Use of DFT, however, is
a computationally taxing process, and its results do not readily provide easily
interpretable insight into the atomic interactions which would be useful
information in material design. An alternative way to address atomic
interactions is to use an interpretable machine learning approach, where a
predictive DFT surrogate is constructed and analyzed. The purpose of this paper
is to propose such a procedure using a modification of the recently published
interpretable distance-based regression method. Our tests with a representative
benchmark set of molecules and a complex hybrid nanoparticle confirm the
viability and usefulness of the proposed approach.
