Year: 2026
Type: preprint
Abstract: We develop a machine learning assisted workflow for the rapid screening of permanent magnet candidates within the CaCu$_5$ structure family. We generated more than 60,000 structures through substitutions. These compositions were screened using a combination of pretrained materials models, Materials Project data, and a machine-learning model for magnetization prediction. Four criteria, namely, metallicity, negative formation energy, energy above the convex hull $\leq 50$~meV/atom, and predicted magnetization above the adopted $\geq 0.75$~T threshold reduced the initial chemical space to 439 candidates for first-principles calculations. Density functional theory calculations revealed that 159 compounds exhibit positive axial magnetic anisotropy energy, favoring [001] over the tested in-plane directions. The resulting dataset reveals chemical trends in magnetic anisotropy across the CaCu$_5$ compositional space and provides a set of candidates for further investigation. More broadly, this work demonstrates a practical strategy for combining existing machine learning models with first-principles calculations to systematically reduce large chemical spaces to tractable sets for computationally intensive materials screening.
Source: arXiv (Cornell University)
Institutions: Oak Ridge National Laboratory, University of Arizona
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: G203625172, G313676650
OpenAlex ID: W7221150548