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A domain-specific machine learning potential model for metallic materials spanning 53 elements
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DOI：10.1038/s41524-026-02072-3.png)
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Abstract
En 中文
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.
Keywords:
Engineering
Materials science
Materials Science
general
Characterization and Evaluation of Materials
Mathematical and Computational Engineering
Theoretical
Mathematical and Computational Physics
Computational Intelligence
Mathematical Modeling and Industrial Mathematics
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