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Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs

Journal Article · · Physical Review Materials
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  1. Ames Laboratory, and Iowa State Univ., Ames, IA (United States)
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The discovery of novel quantum materials within ternary phase spaces containing antagonistic pairs 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.
Research Organization:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Organization:
National Energy Research Scientific Computing Center (NERSC); 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:
2565767
Alternate ID(s):
OSTI ID: 2571560
Journal Information:
Physical Review Materials, Journal Name: Physical Review Materials Journal Issue: 5 Vol. 9; ISSN 2475-9953
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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