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

TL;DR

This study uses machine learning and first-principles calculations to predict and identify new stable and metastable Ce-based ternary compounds involving antagonistic pairs, advancing quantum materials discovery.

Contribution

It introduces a ML-guided framework combining CGCNN and first-principles methods to efficiently discover new Ce-Fe-X compounds with potential technological applications.

Findings

Predicted 9 stable Ce-Fe-X compounds

Identified 37 metastable Ce-Fe-X compounds

Discovered new phases with promising properties

Abstract

The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair 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.