Abstract
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.
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Data availability
The ML-HTP candidate list and DFT validation results are included in the Supplementary Information as Tables S3 and S4. The complete set of all screened compounds, along with ML-predicted properties, will be made available through the HeuslerDB database at https://www.nims.go.jp/group/spintheory/.
Code availability
The developed packages MLIP-HOT and MLIP-FTL will be made available through the Spin Theory Group GitHub repository at https://github.com/nims-spin-theory and our group website at https://www.nims.go.jp/group/spintheory/.
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Acknowledgements
This study used computational resources of the supercomputer Fugaku provided by the RIKEN Center for Computational Science (Project ID: hp250229), the computer resources provided by ISSP, U-Tokyo under the program of SCCMS, and the computer resources at NIMS Numerical Materials Simulator. This study was supported by MEXT Program: Data Creation and Utilization-Type Material Research and Development Project (Digital Transformation Initiative Center for Magnetic Materials) Grant Number JPMXP1122715503 and as “Program for Promoting Researches on the Supercomputer Fugaku” (Data-Driven Research Methods Development and Materials Innovation Led by Computational Materials Science, JPMXP1020230327).
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T.T. conceptualized, designed, and supervised the project; reviewed and edited the manuscript. T.T. and E.X. developed the methodology and code implementation; performed the calculations and analysis; E.X. drafted the manuscript.
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Xiao, E., Tadano, T. Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark. npj Comput Mater (2026). https://doi.org/10.1038/s41524-026-02013-0
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DOI: https://doi.org/10.1038/s41524-026-02013-0
