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<p>Alloys have been the cornerstone of human societal progress, from the Bronze Age to modern sustainable technologies. Yet, their atomic-scale behavior remains poorly understood, impeding their targeted optimization. Thus, reliable and efficient material design tools are urgently needed to accelerate alloy development. To address this demand, we develop a domain-specific machine learning potential (MLP) model spanning 53 metallic elements with balanced accuracy and efficiency. The model achieves DFT-level precision: energy mean absolute error (MAE) = 12 meV/atom, force MAE = 144 meV/Å, accurately predicts lattice parameters, elastic constants, and equation of states. We further validate its versatility through four alloy systems: (1) negative thermal expansion in Ti-Nb orthorhombic phases, (2) the Elinvar effect in Co<sub>25</sub>Ni<sub>25</sub>(TiZrHf)<sub>50</sub> intermetallic compound, (3) grain boundary segregation and high-temperature deformation in NbTaMoW multi-principal element alloy, and (4) precipitation pathway and <i>θ</i>′/Al interface segregation in Al-Cu-based alloy. This model provides a foundational tool for atomic-scale simulation, advancing materials research and accelerating alloy design.</p>

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A domain-specific machine learning potential model for metallic materials spanning 53 elements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月10日

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  • Xin-Yang Li,
  • Jing Li,
  • Yi-Nan Wang,
  • Na-Min Xiao,
  • Wen-Yue Zhao,
  • Zhang-Zhi Shi,
  • Xin-Fu Gu,
  • Fu-Zhi Dai,
  • Lu-Ning Wang

摘要

Alloys have been the cornerstone of human societal progress, from the Bronze Age to modern sustainable technologies. Yet, their atomic-scale behavior remains poorly understood, impeding their targeted optimization. Thus, reliable and efficient material design tools are urgently needed to accelerate alloy development. To address this demand, we develop a domain-specific machine learning potential (MLP) model spanning 53 metallic elements with balanced accuracy and efficiency. The model achieves DFT-level precision: energy mean absolute error (MAE) = 12 meV/atom, force MAE = 144 meV/Å, accurately predicts lattice parameters, elastic constants, and equation of states. We further validate its versatility through four alloy systems: (1) negative thermal expansion in Ti-Nb orthorhombic phases, (2) the Elinvar effect in Co25Ni25(TiZrHf)50 intermetallic compound, (3) grain boundary segregation and high-temperature deformation in NbTaMoW multi-principal element alloy, and (4) precipitation pathway and θ′/Al interface segregation in Al-Cu-based alloy. This model provides a foundational tool for atomic-scale simulation, advancing materials research and accelerating alloy design.