  The discovery of novel quantum materials within ternary phase spaces
containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents
significant challenges yet holds great potential. In this work, we investigate
the stabilization of these immiscible pairs through the integration of Cerium
(Ce), an abundant rare-earth and cost-effective element. By employing a machine
learning (ML)-guided framework, particularly crystal graph convolutional neural
networks (CGCNN), combined with first-principles calculations, we efficiently
explore the composition/structure space and predict 9 stable and 37 metastable
Ce-Fe-X (X=Bi, Pb, In and Ag) ternary compounds. Our findings include the
identification of multiple new stable and metastable phases, which are
evaluated for their structural and energetic properties. These discoveries not
only contribute to the advancement of quantum materials but also offer viable
alternatives to critical rare earth elements, underscoring the importance of
Ce-based intermetallic compounds in technological applications.
