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<p>While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared with representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion and protein–ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural and spectral properties of two-dimensional materials, metallic glasses and organic crystals.</p>

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NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Ting Liang,
  • Ke Xu,
  • Eric Lindgren,
  • Zherui Chen,
  • Rui Zhao,
  • Jiahui Liu,
  • Esmée Berger,
  • Benrui Tang,
  • Bohan Zhang,
  • Yanzhou Wang,
  • Keke Song,
  • Penghua Ying,
  • Nan Xu,
  • Haikuan Dong,
  • Shunda Chen,
  • Paul Erhart,
  • Zheyong Fan,
  • Tapio Ala-Nissila,
  • Jianbin Xu

摘要

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared with representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion and protein–ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural and spectral properties of two-dimensional materials, metallic glasses and organic crystals.