2025/10/21 by J.M. Moree, Ryotaro Arita, Morée, Jean-Baptiste +3
Materials Science · #FOS: Physical sciences #Heusler alloys: electronic and magnetic properties #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Thermal Expansion and Ionic Conductivity
paper · pdf · doi:10.48550/arxiv.2510.18469
openalex publication_date 2025/10/21 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
We employ interpretable machine learning to analyze the material dependence of the magnetic transition temperature Tc in ferromagnetic and ferrimagnetic Heusler compounds. For over 200 candidate materials with the same F43m crystal structure but different chemical formulae and lattice constants, we consider both experimental Tc and those computed via classical Monte Carlo simulations using magnetic interactions derived from ab initio calculations. We use the hierarchical dependence extraction (HDE) procedure [Morée and Arita, Phys. Rev. B 110, 014502 (2024)] to determine how Tc depends on chemical composition and magnetic moments, from leading to higher-order effects, and use these dependencies to construct an explicit expression for Tc. Our results show that the HDE framework predicts Tc with accuracy comparable to other machine-learning approaches such as neural network and random forest algorithms while remaining fully interpretable. Tc is primarily governed by the proportions of Fe, Co, and Mn, increasing systematically with their concentration. These findings clarify how chemical composition and magnetic moments influence Tc in collinear Heusler alloys and support the use of the HDE for computationally guided discovery of new functional materials with tailored Tc values.