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

Journal Article · · Inorganic Chemistry

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. Firstprinciples 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.

Research Organization:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
Grant/Contract Number:
AC02-07CH11358
OSTI ID:
2565475
Journal Information:
Inorganic Chemistry, Journal Name: Inorganic Chemistry Journal Issue: 20 Vol. 64; ISSN 0020-1669; ISSN 1520-510X
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

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