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Description:

We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy (Eaniso). Structure optimization and evaluation of formation energy and energy above the convex hull were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and Eaniso were predicted by eSEN models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Heusler alloys, Machine-learning potential, Transfer learning, First-principles calculation, Curie temperature, Magnetic anisotropy energy

Date published: 2026-02-19

Publisher: Springer Science and Business Media LLC

Journal:

• npj Computational Materials (ISSN: 20573960) vol. 12 issue. 1 133

Funding:

• Ministry of Education, Culture, Sports, Science and Technology JPMXP1020230327
• Ministry of Education, Culture, Sports, Science and Technology JPMXP1122715503

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1038/s41524-026-02013-0

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Updated at: 2026-03-30 13:06:06 +0900

Published on MDR: 2026-03-30 16:24:37 +0900