Abstract
Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce-Co-Cu ternary compounds using a machine learning (ML)-guided framework integrated with first-principles calculations. We employ a crystal graph convolutional neural network (CGCNN), which enables efficient screening for promising candidates, significantly accelerating the material discovery process. With this approach, we predict five stable compounds, Ce(3)Co(3)Cu, CeCoCu(2), Ce(12)Co(7)Cu, Ce(11)Co(9)Cu, and Ce(10)Co(11)Cu(4), with formation energies below the convex hull, along with hundreds of low-energy (possibly metastable) Ce-Co-Cu ternary compounds. First-principles calculations reveal that several structures are both energetically and dynamically stable. Notably, two Co-rich low-energy compounds, Ce(4)Co(33)Cu and Ce(4)Co(31)Cu(3), are predicted to have high magnetizations.