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<p>We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-<i>d</i> Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy (<i>E</i><sub>aniso</sub>). 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 <i>E</i><sub>aniso</sub> 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.</p>

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Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Enda Xiao,
  • Terumasa Tadano

摘要

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.