Search for Stable and Low-Energy Ce–Co–Cu Ternary Compounds Using Machine Learning

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, Ce₃Co₃Cu, CeCoCu₂, Ce₁₂Co₇Cu, Ce₁₁Co₉Cu, and Ce₁₀Co₁₁Cu₄, 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, Ce₄Co₃₃Cu and Ce₄Co₃₁Cu₃, are predicted to have high magnetizations.

A crystal graph convolutional neural network approach is employed for predicting and efficiently screening candidates that are both energetically and dynamically stable and have a high magnetization.