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<p>Silicon nitride (Si<sub>3</sub>N<sub>4</sub>) is a strong, thermally stable covalent ceramic that is typically regarded as brittle with limited deformability. A recent experimental study combined with density functional theory (DFT) indicates that the <i>α/β</i> interface undergoes a <i>β</i>→<i>α</i> transformation via sliding followed by bond-switching, suggesting a pathway to achieve plasticity, but DFT’s spatiotemporal reach prevents a full mechanistic picture. Here, we develop a physics-informed high-accuracy neural network interatomic potential (NNAP) model with DFT-level accuracy for the phase transformations and use it to perform large-scale atomistic simulations. NNAP-guided simulations show that structural relaxation during the relative sliding between <i>α</i>- and <i>β</i>-phases at the interface triggers pronounced atomic-layer rearrangements and lowers the energy barrier by nearly 60%. We further find that the ensuing phase transformation does not proceed by isolated layer-by-layer switching but instead follows in-plane nucleation and growth mediated by a bilayer cooperative mechanism, which further reduces kinetic barriers and facilitates the transformation. These results provide new atomistic insights into interface-driven phase transformations in dual-phase Si<sub>3</sub>N<sub>4</sub> and offer guidance for designing more deformable covalent ceramics.</p>

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Unraveling the bilayer-cooperative transformation mechanism at the α/β-Si3N4 interface via machine-learning simulations

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Yuxuan Chen,
  • Guanchen Dong,
  • Qing-an Li,
  • Huanrong Liu,
  • Rui Su,
  • Pengfei Guan

摘要

Silicon nitride (Si3N4) is a strong, thermally stable covalent ceramic that is typically regarded as brittle with limited deformability. A recent experimental study combined with density functional theory (DFT) indicates that the α/β interface undergoes a β→α transformation via sliding followed by bond-switching, suggesting a pathway to achieve plasticity, but DFT’s spatiotemporal reach prevents a full mechanistic picture. Here, we develop a physics-informed high-accuracy neural network interatomic potential (NNAP) model with DFT-level accuracy for the phase transformations and use it to perform large-scale atomistic simulations. NNAP-guided simulations show that structural relaxation during the relative sliding between α- and β-phases at the interface triggers pronounced atomic-layer rearrangements and lowers the energy barrier by nearly 60%. We further find that the ensuing phase transformation does not proceed by isolated layer-by-layer switching but instead follows in-plane nucleation and growth mediated by a bilayer cooperative mechanism, which further reduces kinetic barriers and facilitates the transformation. These results provide new atomistic insights into interface-driven phase transformations in dual-phase Si3N4 and offer guidance for designing more deformable covalent ceramics.