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<p>Predicting the synthetic accessibility of multi-principal element alloys (MPEAs) across the global chemical space remains a challenge. In this study, we show that the synthesizability of MPEAs across broad compositional and structural spaces can be predicted using a physical model that expresses the total energy of any MPEA as a linear combination of energies from lower-dimensional subsystems. The model is validated with a large computational dataset and supported by the experimental synthesis of multiple MPEAs, achieving mean absolute errors near or below 7 meV/atom on a density functional theory dataset of 135,791 MPEAs spanning 28 metals and up to ten components. Its accuracy is comparable to state-of-the-art deep learning models while maintaining interpretability through cluster-expansion theory. Moreover, we show that the stability of high-entropy alloys can be predicted using a linear combination of energies from lower-dimensional systems with low errors, indicating a flatter energy landscape at high compositional complexity.</p>

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Universal framework for efficient estimation of stability in multi-principal element alloys

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

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  • Lin Wang,
  • Bo Shen,
  • Zheng-Da He,
  • Zihao Ye,
  • Yan Zeng,
  • Chad A. Mirkin,
  • Bin Ouyang

摘要

Predicting the synthetic accessibility of multi-principal element alloys (MPEAs) across the global chemical space remains a challenge. In this study, we show that the synthesizability of MPEAs across broad compositional and structural spaces can be predicted using a physical model that expresses the total energy of any MPEA as a linear combination of energies from lower-dimensional subsystems. The model is validated with a large computational dataset and supported by the experimental synthesis of multiple MPEAs, achieving mean absolute errors near or below 7 meV/atom on a density functional theory dataset of 135,791 MPEAs spanning 28 metals and up to ten components. Its accuracy is comparable to state-of-the-art deep learning models while maintaining interpretability through cluster-expansion theory. Moreover, we show that the stability of high-entropy alloys can be predicted using a linear combination of energies from lower-dimensional systems with low errors, indicating a flatter energy landscape at high compositional complexity.