  We employ a descriptor based machine-learning approach to assess the effect
of chemical alloying on formation-enthalpy of rare-earth intermetallics.
Application of machine-learning approaches in rare-earth intermetallic design
have been sparse due to limited availability of reliable datasets. In this
work, we developed an `in-house' rare-earth database with more than 600$+$
compounds, each entry was populated with formation enthalpy and related atomic
features using high-throughput density-functional theory (DFT). The SISSO (sure
independence screening and sparsifying operator) based machine-learning method
with meaningful atomic features was used for training and testing the formation
enthalpies of rare earth compounds. The complex lattice function coupled with
the machine-learning model was used to explore the effect of transition metal
alloying on the energy stability of Ce based cubic Laves phases (MgCu$_{2}$
type). The SISSO predictions show good agreement with high-fidelity DFT
calculations and X$-$ray powder diffraction measurements. Our study provides
quantitative guidance for compositional considerations within a
machine-learning model and discovering new metastable materials. The
electronic-structure of Ce$-$Fe$-$Cu based compound was also analyzed
in$-$depth to understand the electronic origin of phase stability. The
interpretable analytical models in combination with density$-$functional theory
and experiments provide a fast and reliable design guide for discovering
technologically useful materials.
