  In the last decade, the use of Machine and Deep Learning (MDL) methods in
Condensed Matter physics has seen a steep increase in the number of problems
tackled and methods employed. A number of distinct MDL approaches have been
employed in many different topics; from prediction of materials properties to
computation of Density Functional Theory potentials and inter-atomic force
fields. In many cases the result is a surrogate model which returns promising
predictions but is opaque on the inner mechanisms of its success. On the other
hand, the typical practitioner looks for answers that are explainable and
provide a clear insight on the mechanisms governing a physical phenomena. In
this work, we describe a proposal to use a sophisticated combination of
traditional Machine Learning methods to obtain an explainable model that
outputs an explicit functional formulation for the material property of
interest. We demonstrate the effectiveness of our methodology in deriving a new
highly accurate expression for the enthalpy of formation of solid solutions of
lanthanides orthophosphates.
