Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy
Year: 2024
Type: article
Abstract: We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.
Source: APL Machine Learning
DOI: 10.1063/5.0208761
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G1286251239, G1418315740, G1519358529, G1581976118, G1866217153, G2316606464, G2327333655, G2441759041, G249831736, G260240254, G285533474, G498139845, G524707868, G5959670421, G6495043141, DMREF: Collaborative Research for the Design and Synthesis of Novel Magnetic Materials, DMREF:SusChEM:Collaborative Research: Design and Synthesis of Novel Magnetic Materials, G7626161611, G969889393
OpenAlex ID: W4403397583