Search for stable and low-energy Ce-Co-Cu ternary compounds using machine learning

TL;DR

This paper uses machine learning and first-principles calculations to identify stable and low-energy Ce-Co-Cu compounds, discovering new candidates with high magnetization potential for permanent magnet applications.

Contribution

It introduces a ML-guided framework with CGCNN for efficient screening of Ce-Co-Cu compounds, predicting new stable and low-energy structures with potential magnetic properties.

Findings

Predicted five stable Ce-Co-Cu compounds with formation energies below the convex hull.

Identified several low-energy, possibly metastable, compounds.

Two Co-rich compounds predicted to have high magnetizations.

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 materials 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.